U.S. patent application number 11/786326 was filed with the patent office on 2008-10-16 for system for building a data structure representing a network of users and advertisers.
This patent application is currently assigned to Yahoo! Inc.. Invention is credited to Tasos Anastasakos, Chi-Chao Chang, Manish Tayal.
Application Number | 20080256056 11/786326 |
Document ID | / |
Family ID | 39854601 |
Filed Date | 2008-10-16 |
United States Patent
Application |
20080256056 |
Kind Code |
A1 |
Chang; Chi-Chao ; et
al. |
October 16, 2008 |
System for building a data structure representing a network of
users and advertisers
Abstract
A system is described for building a data structure representing
a network of advertisers and users. The system may include a memory
and a processor. The memory may be operatively connected to the
processor and may store a historical dataset comprising of a
plurality of query items and advertisement items, a plurality of
query-advertisement link items, a weight, a data structure and a
condition. The processor may identify the historical dataset, and
link the query items to the advertisement items to generate
query-advertisement link items. The processor may determine the
weight of each query-advertisement link item and may store the
query-advertisement link items and the weight in the data structure
if the query-advertisement link item satisfies the condition.
Inventors: |
Chang; Chi-Chao; (Santa
Clara, CA) ; Tayal; Manish; (Santa Clara, CA)
; Anastasakos; Tasos; (San Jose, CA) |
Correspondence
Address: |
BRINKS HOFER GILSON & LIONE / YAHOO! OVERTURE
P.O. BOX 10395
CHICAGO
IL
60610
US
|
Assignee: |
Yahoo! Inc.
|
Family ID: |
39854601 |
Appl. No.: |
11/786326 |
Filed: |
April 10, 2007 |
Current U.S.
Class: |
1/1 ;
707/999.005 |
Current CPC
Class: |
G06Q 30/0244 20130101;
G06Q 30/0256 20130101; G06Q 30/02 20130101; G06Q 30/0277
20130101 |
Class at
Publication: |
707/5 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for building a data structure representing a network of
users and advertisers, the method comprising: identifying a
historical dataset comprising a plurality of query items wherein
each query item is associated with a set of advertisement items;
linking each query item in the plurality of query items to each
advertisement item in the set of advertisement items associated
with the query item to generate a plurality of query-advertisement
link items; determining a weight for each query-advertisement link
item; storing each query-advertisement link item and the weight
calculated for each query-advertisement link item in a data
structure if the query-advertisement link item satisfies a
condition.
2. The method of claim 1 wherein each query item in the plurality
of query items comprises a search performed by a user.
3. The method of claim 2 wherein the set of advertisement items
associated with each query item comprises an ad listing of an
advertiser that was clicked on by the user after searching for the
query item.
4. The method of claim 1 wherein the condition is satisfied for
each query-advertisement link item if the query advertisement link
item does not exist in the data structure.
5. The method of claim 1 wherein the weight of each
query-advertisement link item comprises a total number of click
throughs attributable to the query-advertisement link item.
6. The method of claim 5 wherein the total number of clicks may
represent the number of times a plurality of users clicked on the
advertisement item associated with the query-advertisement link
item after searching for the query item associated with the
query-advertisement link item.
7. The method of claim 5 wherein the total number of click throughs
for the query-advertisement link item comprises the total number of
equivalent query-advertisement link items in the historical
dataset.
8. The method of claim 1 wherein the data structure comprises a
database table.
9. The method of claim 1 further comprising rebuilding the data
structure at a regular interval of time with an updated historical
dataset.
10. A method of constructing a bipartite graph out of click log
data, comprising: identifying a set of unique queries in the click
log data; creating a set of query nodes in a bipartite graph
wherein each node comprises a query in the set of unique queries;
identifying a set of unique ads in the click log data; creating a
set of ad nodes in the bipartite graph wherein each ad node
comprises an ad in the set of unique ads; identifying a set of
clicks in the click log data wherein each click comprises a query
and an ad; building an edge from each query node in the set of
query nodes to each ad node in the set of ad nodes if the set of
clicks contains a click comprising the query node and the ad node;
and storing the query nodes in the bipartite graph, the ad nodes in
the bipartite graph, and each built edge in a data structure.
11. The method of claim 10 wherein the edge comprises a weight.
12. The method of claim 11 wherein the weight comprises a total
number of click throughs.
13. The method of claim 10 wherein the data structure comprises a
database.
14. The method of claim 10 further comprising rebuilding the
bipartite graph after a period of time T.
15. A system for building a data structure representing a network
of users and advertisers, comprising: a memory to store a
historical dataset comprising a plurality of query items wherein
each query item is associated with a set of advertisement items, a
plurality of query-advertisement link items, a weight, a data
structure, and a condition; and a processor operatively connected
to the memory, the processor operative to identify the historical
dataset, link each query item in the plurality of query items to
each advertisement item in the set of advertisement items
associated with the query item to generate a plurality of
query-advertisement link items, determine the weight of each
query-advertisement link item and store each query-advertisement
link item and the weight in the data structure if the
query-advertisement link item satisfies the condition.
16. The system of claim 15 wherein each query item in the plurality
of query items comprises a search performed by a user.
17. The system of claim 16 wherein the set of advertisement items
associated with each query item comprises an ad listing of an
advertiser that was clicked on by the user after searching for the
query item.
18. The system of claim 15 wherein the condition is satisfied for
each query-advertisement link item if the query advertisement link
item does not exist in the data structure.
19. The system of claim 15 wherein the weight of each
query-advertisement link item comprises a total number of click
throughs attributable to the query-advertisement link item.
20. The method of claim 15 wherein the data structure comprises a
database table.
Description
COPYRIGHT NOTICE
[0001] A portion of the disclosure of this patent document contains
material that is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or the patent disclosure, as it appears in the
Patent and Trademark Office patent files or records, but otherwise
reserves all copyright rights whatsoever.
TECHNICAL FIELD
[0002] The present description relates generally to a system and
method, generally referred to as a system, for building a data
structure representing a network of advertisers and users, enabling
the application of collaborative filtering and learning techniques
to optimize the allocation of online advertisements.
BACKGROUND
[0003] Online advertising may be an important source of revenue for
enterprises engaged in electronic commerce. The advent of search
engines may have resulted in an increase in the use of sponsored
search, or paid search, by advertisers. Sponsored search may be an
arrangement where companies and/or individuals pay a service
provider for placement of their advertisement listing. The
advertisement listing may be placed in a search result set
generated by the service provider's search engine or may be placed
on a page of a partner of the service provider, e.g., a blog. An
advertiser may place bids for one or more keywords within a search
term bidding marketplace that may work in conjunction with one or
more search engines. An advertiser may bid on keywords that may
indicate an interest in the products, services, information, etc.
being advertised in the advertisement. The amount an advertiser may
bid on the keywords may indicate the cost the advertiser may be
willing to pay for placement of the advertisement.
[0004] A user may submit a query comprising one or more keywords to
a search engine and the search engine may produce a result set
comprising one or more listings that may fall within the scope of
the query, including sponsored search listings. The search engine
may use the keywords, as well as other features such as user and
advertiser information, to select sponsored search listings for
inclusion in the result set. The user may generate a lead for an
advertiser when the user selects the sponsored listing of the
advertiser, such as by clicking on the advertisement.
[0005] Search engines may strive to maintain an increasing supply
of users to deliver valuable leads to advertisers and advertisers,
in turn, may demand a growing supply of leads from search engines.
This may result in growth of search engine usage and online
advertising budgets. Search engines retain and increase their
supply of users by providing relevant web search results and
advertising. Advertisers may increase their demand of leads as lead
quality and targeting improve. A marketplace therefore may exist
that includes a given keyword, the set of one or more users who may
provide search queries comprising the keyword over a given period
of time ("lead supply") and the advertisers who may compete for
leads (or clicks) for the given keyword. Search engines or other
advertisement providers may use the above-described term bidding
marketplace, which is a form of an auction, to allocate leads to
advertisers.
[0006] In a "dense" marketplace, advertiser demand may exceed the
supply of leads. The auction may be designed such that advertisers
who are most relevant to the keyword, and/or value the lead the
most, may place the highest bid on the keyword. In "shallow" or
"sparse" marketplaces, advertiser demand may not exceed the supply
of leads. A shallow marketplace may have limited leads because the
marketplace may be characterized by multiple keyword phrases, as
well as keywords that may be obscure and/or may have a very narrow
context or intent. Since there may be only a small number of
advertisers bidding for these keywords, the average cost per click
for a given lead may be generally low. Advertisers may bombard
search engines with low bids for a large number of such keywords to
capture opportunities in shallow marketplaces. The imbalances of
supply and demand may lead to inadequate overall relevance to users
and a lack of competition among advertisers, ultimately resulting
in a decrease in revenue to the service provider.
[0007] Furthermore the term-bidding marketplace may require
advertisers to predict keywords or queries that may be searched for
by users. If a user searches for a keyword or query which has not
been bid on by any advertisers, the search engine may not display
any advertisements to the user. If a search results page is
displayed to a user with no advertisements, there may be little
likelihood of leads for the advertisers and revenue for the search
engine provider.
SUMMARY
[0008] A system for building a data structure representing a
network of advertisers and users may include a memory and a
processor. The memory may be operatively connected to the processor
and may store a historical dataset comprising of a plurality of
query items and advertisement items, a plurality of
query-advertisement link items, a weight, a data structure and a
condition. The processor may identify the historical dataset, link
the query items to the advertisement items to generate
query-advertisement link items. The processor may determine the
weight of each query-advertisement link item and may store the
query-advertisement link item and the weight in the data structure
if the query-advertisement link item satisfies the condition.
[0009] A method for building a data structure representing a
network of users and advertisers may identify a historical dataset
containing a plurality of query items and advertisement items. The
query items may be linked to the advertisement items to generate a
plurality of query-advertisement link items. A weight may be
determined for each query-advertisement link item and both the
weight and the query-advertisement link item may be stored in a
data structure if a condition is met.
[0010] A method of constructing a bipartite graph out of click log
data may identify a set of queries in the click log data. A set of
query and ad nodes in a bipartite graph may be created from ads and
queries identified in the click log data. A set of clicks may be
identified in the click log data linking the queries and ads. An
edge may be built in the bipartite graph, connecting a query and an
ad, if click data exists linking the query and ad. The query nodes,
ad nodes and edges may be stored in a data structure.
[0011] Other systems, methods, features and advantages will be, or
will become, apparent to one with skill in the art upon examination
of the following figures and detailed description. It is intended
that all such additional systems, methods, features and advantages
be included within this description, be within the scope of the
embodiments, and be protected by the following claims and be
defined by the following claims. Further aspects and advantages are
discussed below in conjunction with the description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The system and/or method may be better understood with
reference to the following drawings and description. Non-limiting
and non-exhaustive descriptions are described with reference to the
following drawings. The components in the figures are not
necessarily to scale, emphasis instead being placed upon
illustrating principles. In the figures, like referenced numerals
may refer to like parts throughout the different figures unless
otherwise specified.
[0013] FIG. 1 is a block diagram of a system for building a data
structure representing a network of advertisers and users.
[0014] FIG. 2 is block diagram of a simplified view of a network
environment implementing the system of FIG. 1 or other systems for
building a data structure representing a network of advertisers and
users.
[0015] FIG. 3 is a block diagram illustrating a system for building
a data structure representing a network of advertisers and
users.
[0016] FIG. 4 is a graph illustrating an example of a network of
advertisers and users built by the system of FIG. 3 or other
systems for generating a network of advertisers and users.
[0017] FIG. 5 is a flowchart illustrating the operations of the
system of FIG. 3, or other systems for building a data structure
representing a network of advertisers and users.
[0018] FIG. 6 is a flowchart illustrating the operations of
identifying the raw context data for a query/advertisement pairing
in the system of FIG. 3, or other systems for building a data
structure representing a network of advertisers and users.
[0019] FIG. 7 is a flowchart illustrating the operations of
building a link between a query and an advertisement in the system
of FIG. 3 or other systems for building a data structure
representing a network of advertisers and users.
[0020] FIG. 8 is a flowchart illustrating the use of a network of
advertisers and users, built by the system of FIG. 3 or other
systems for building a data structure representing a network of
advertisers and users, to suggest queries related to a query.
[0021] FIG. 9 is a flowchart illustrating the use of a data
structure representing a network of advertisers and users, built by
the system of FIG. 3 or other systems for building networks of
advertisers and users, to determine ad listings relevant to a
query.
[0022] FIG. 10 is a flowchart illustrating the use of a data
structure representing a network of advertisers and users, built by
the system of FIG. 3 or other systems for building networks of
advertisers and users, to determine the value of a suggested
query.
[0023] FIG. 11 is a flowchart illustrating the use of a data
structure representing a network of advertisers and users, built by
the system of FIG. 3 or other systems for building networks of
advertisers and users, to determine the value of a matching system
and a suggested query.
[0024] FIG. 12 is a flowchart illustrating the use of a data
structure representing a network of advertisers and users, built by
the system of FIG. 3 or other systems for building networks of
advertisers and users, to integrate valuable query suggestions with
experimental query suggestions.
[0025] FIG. 13 is an illustration of an exemplary page displaying
advertisements.
[0026] FIG. 14 is a screenshot of a search results page displaying
advertisements.
[0027] FIG. 15 is an illustration a general computer system that
may be used in the system of FIG. 3 or other systems for building
data structures representing a network of advertisers and
users.
DETAILED DESCRIPTION
[0028] A system and method, generally referred to as a system,
relate to building a data structure representing a network of
advertisers and users, and more particularly, but not exclusively,
to building a data structure representing a network which may
provide a platform for combining dense and shallow search term
marketplaces to aggregate supply and demand, increasing overall
relevance to users and competition among advertisers. Combining the
marketplaces may increase the aggregate value of sponsored search
to a service provider due to a higher supply of users, advertiser
demand, and price per lead. The principles described herein may be
embodied in many different forms.
[0029] The system may build a data structure representing a network
of users and advertisers based on advertiser intent described by
target queries, valuation and spend, as well as historical user
behavior described by queries, user profiles and other context. A
query may refer to a set of terms searched for by a user or a set
of terms related to the content of a page, such as a web page
displayed to a user. The network may be independent of the language
and other regional characteristics of the underlying data, enabling
a plurality of networks to be combined across markets defined by
language and other regional characteristics.
[0030] The network may be used to identify advertisements to be
served by a search engine, such as supplemental advertisements
related to the user's search query. The network may be used to
estimate the relative quality of advertisements and evolve a
quality benchmark, such as a quality benchmark based on
advertisement performance and/or user feedback. The additional
advertisements may increase the depth and competitiveness of
shallow keywords by eliciting/inducing more overall user
attention.
[0031] The network may further be used to generate keyword
suggestions to be queried at advertisement serving time, and/or to
be presented to advertisers during campaign management. The network
may be used to evaluate the quality (relevance, value) of keywords
suggested through the use of the network, and keywords suggested
through other matching techniques, in the first and higher orders.
The network may be used to utilize high quality keyword suggestions
and further to explore unknown or low value suggestions scheduled
by some measure based on a relevance model. The keyword suggestions
may increase the depth and competitiveness of shallow keywords by
eliciting/inducing more overall user attention.
[0032] The network may be utilized in several ways to suggest
keywords and identify advertisements. The network may be used
capture the semantic knowledge gap between raw user queries (often
syntactically different) and underlying implicit user intent in an
automated, non-intrusive, implicit way. The captured semantic
knowledge gap may be utilized to suggest keywords and/or identify
advertisements.
[0033] The network may be analyzed to identify both significantly
unrelated and significantly related sub-networks based on some
affinity measure, such as keyword semantic affinities, advertiser
online spent, and past historical performance based on user clicks
and/or revenue generated. The sub-network relationships may be
utilized to suggest keywords and/or identify advertisements.
[0034] The network may be extendable to account for new emerging
forms of advertisement performance feedback, such as clicks,
various forms of conversions, and/or any other metric for measuring
advertisement performance. Single or multiple forms of
advertisement feedback may be translated into semantic knowledge.
The semantic knowledge may be utilized to suggest keywords and/or
identify advertisements.
[0035] The network may be adaptable to account for temporal
increments given increasing advertiser participation, changes in
advertiser intent, valuation and online spend, changes in user
behavior, demographics and mix, changes in aggregate user intent,
search usage, and mix. The adapted network may be capable of
capturing temporal shifts in user intent, advertiser intent and/or
other context and a corresponding shift in the underlying semantic
knowledge. The shift in semantic knowledge may be utilized to
suggest keywords and/or identify advertisements, such as by
implicitly capturing language seasonal patterns, and language usage
patterns.
[0036] FIG. 1 provides a general overview of a system 100 for
building a data structure representing a network of advertisers and
users. Not all of the depicted components may be required, however,
and some implementations may include additional components.
Variations in the arrangement and type of the components may be
made without departing from the spirit or scope of the claims as
set forth herein. Additional, different or fewer components may be
provided.
[0037] The system 100 may include one or more revenue generators
110A-N, such as advertisers, a service provider 130, such as a
search engine marketing service provider, and one or more users
120A-N, such as web surfers or consumers. The service provider 130
may implement an advertising campaign management system
incorporating an auction based and/or non-auction based
advertisement serving system. The revenue generators 110A-N may pay
the service provider 130 to serve, or display, advertisements of
their goods or services, such as on-line advertisements, on a
network, such as the Internet. The advertisements may include
sponsored listings, banners ads, popup advertisements, or generally
any way of attracting the users 120A-N to the web site of the
revenue generators 110A-N.
[0038] The amount the revenue generators 110A-N may pay the service
provider 130 may be based on one or more factors. These factors may
include impressions, click throughs, conversions, and/or generally
any metric relating to the advertisement and/or the behavior of the
users 120A-N. The impressions may refer to the number of times an
advertisement may have been displayed to the users 120A-N. The
click throughs may refer to the number of times the users 120A-N
may have clicked through an advertisement to a web site of one of
the revenue generators 110A-N, such as the revenue generator A
110A. The conversions may refer to the number of times a desired
action was taken by the users 120A-N after clicking though to a web
site of the revenue generator A 110A. The desired actions may
include submitting a sales lead, making a purchase, viewing a key
page of the site, downloading a whitepaper, and/or any other
measurable action. If the desired action is making a purchase, then
the revenue generator A 110A may pay the service provider 130 a
percentage of the purchase.
[0039] The users 120A-N may be consumers of goods or services who
may be searching for a business, such as the business of one of the
revenue generators 110A-N. Alternatively or in addition the users
120A-N may be machines or other servers, such as the third party
server 250. The users 120A-N may supply information describing
themselves to the service provider 130, such as the location,
gender, or age of the users 120A-N, or generally any information
that may be required for the users 120A-N to utilize the services
provided by the service provider 130.
[0040] In the system 100, the revenue generators 110A-N may
interact with the service provider 130, such as via a web
application. The revenue generators 110A-N may send information,
such as billing, website and advertisement information, to the
service provider 130 via the web application. The web application
may include a web browser or other application such as any
application capable of displaying web content. The application may
be implemented with a processor such as a personal computer,
personal digital assistant, mobile phone, or any other machine
capable of implementing a web application.
[0041] The users 120A-N may also interact individually with the
service provider 130, such as via a web application. The users
120A-N may interact with the service provider 130 via a web based
application or a standalone application. The service provider 130
may communicate data to the revenue generators 110A-N and the users
120A-N over a network. The following examples may refer to a
revenue generator A 110A as an online advertiser; however the
system 100 may apply to any revenue generators 110A-N who may
benefit from a network of advertisers and users, such as a service
provider partner.
[0042] One example of a service provider partner may be a content
publisher. Content publishers may be service provider partners who
may display content, such as news articles, videos, or any other
type of content to the users 120A-N. Along with the content,
content publishers may display advertisements of the advertisers to
the users 120A-N. The service provider 130 may supply the
advertisements to the content publishers. The advertisements may
relate to the content displayed on the page, or the advertisements
may relate to the characteristics, demographics and/or
login-profiles of the users 120A-N. When the users 120A-N interact
with an advertisement of one of the advertisers, the advertisers
may pay the service provider 130. The service provider 130 may in
turn pay the content publisher. Thus the revenue generators 110A-N
may include one or more content publishers, advertisers, and/or
other service provider partners.
[0043] In operation, one of the revenue generators 110A-N, such as
revenue generator A 110A, may provide information to the service
provider 130. This information may relate to the transaction taking
place between the revenue generator A 110A and the service provider
130, or may relate to an account the revenue A 110A generator
maintains with the service provider 130. In the case of a revenue
generator A 110A who is an online advertiser, the revenue generator
A 110A may provide initial information necessary to open an account
with the service provider 130. The revenue generators 110A-N may
implement one or more advertising tactics with the service provider
130 to target advertisements to the users 120A-N and/or the revenue
generators 110A-N may authorize the service provider 130 to use any
advertising tactic, or method, to display their advertisements to
the users 120A-N.
[0044] One example of an advertising tactic may be sponsored
search, such as targeting advertisements to search terms or
keywords. Sponsored search may operate within the context of an
auction-based system or marketplace that may be used by the revenue
generators 110A-N to bid for search terms or queries. When the
terms are used in a search, the ad listings or links of a revenue
generator, such as the revenue generator A 1110A, may be displayed
among the search results. Revenue generators 110A-N may further bid
for position or prominence of their listings in the search results.
With regard to auction-based sponsored search, the revenue
generator A 110A may provide a uniform resource locator (URL) for
the webpage to which the ad may take the users 120A-N to if clicked
on. The revenue generator A 110A may also provide the text or
creative of the advertisement that may be displayed in connection
with the URL. A revenue generator A 110A may identify one or more
terms that may be associated with the advertisement.
[0045] Another example of an advertising tactic may be content
matching. Content match advertisements may be used by the revenue
generator A 1110A to complement, or as alternative to, the
sponsored search tactic. Ads stored according to the content match
tactic may be displayed alongside relevant articles, product
reviews, etc, presented to the users 120A-N by the service provider
130 or a service provider partner, such as a content publisher. The
system 100 may implement a content matching system. The content
matching system may process the words on a given page to determine
a set of terms. The set of terms may be the most commonly occurring
words, or may be determined by some other factor. The set of terms
may then be used to determine which of the content match
advertisements to display. The content matching system may use the
set of terms to select advertisements, such as by selecting the
advertisements which contain the most number of words matching the
set of terms. The set of terms may be referred to as a query or a
content match query.
[0046] Content match advertisements may be displayed on any web
page containing content relevant to the advertisement. For the
content match tactic, the revenue generator A 110A may provide one
or more URLs identifying the address of a webpage a given ad may
take the users 120A-N to if clicked on. The revenue generator A
110A may also provide the text, image, video or other type of
multimedia comprising the creative portion of the advertisement
that may be displayed next to the URL.
[0047] Another example of an advertising tactic may be a banner
advertisement or popup advertisement. The banner ad and/or popup ad
tactic may be used by the revenue generators 110A-N to complement,
or as alternative to, the sponsored search tactic and the content
match tactic. In contrast to the sponsored search tactic and
content match tactic, which may be based on a pay-per-click payment
scheme, a revenue generator 110A-N may pay for every display of a
banner ad and/or popup ad, referred to as an impression.
Alternatively, if the banner ad and/or popup ad displays a phone
number, a revenue generator, such as the revenue generator A 110A
may only be billed if a user, such as the user A 120A, calls the
phone number associated with the advertisement ("pay-per-call").
Thus, for the banner ad and/or popup ad tactic, the revenue
generator A 110A may provider a URL to the webpage where the ad may
take the user A 120A if clicked on, as well as the creative or the
given banner ad and/or popup ad.
[0048] A revenue generator A 110A who is an online advertiser may
maintain several accounts with the service provider 130. For each
account the revenue generator A 110A may maintain several
advertising campaigns, such as an MP3 player campaign, a car
campaign, or any other distinguishable category of products and/or
services. Each campaign may include one or more ad groups. The ad
groups may further distinguish the category of products and/or
services represented in the advertising campaign, such as by search
tactic, performance parameter, demographic of user, family of
products, or almost any other parameter desired by the revenue
generators 110A-N.
[0049] For example, if the advertising campaign is for MP3 Players,
there may be an ad group each brand of MP3 players, such as APPLE
IPOD.RTM. or MICROSOFT ZUNE.RTM.. Allowing the revenue generators
110A-N to determine their own ad groups may allow the service
provider 130 to provide more useful information to the revenue
generators 110A-N. The revenue generators 110A-N may thereby
display, manage, optimize, or view reports on, advertisement
campaign information in a manner most relevant to a revenue
generator, such as the revenue generator A 110A.
[0050] The ad groups may include one or more listings. A listing
may include a title, a description, one or more search keywords, an
advertisement, a destination URL, and a bid amount. A listing may
represent an association between the one or more search keywords
identified by the revenue generator A 110A, and an advertisement of
the revenue generator A 110A.
[0051] The title may be the name of the product being advertised,
such as "JEEP WRANGLER.RTM.." The description may describe the
product being advertised. For example, if DAIMLERCHRYSLER.RTM.
wished to advertise a DAIMLERCHRYSLER JEEP WRANGLER.RTM., the
listing may have a description of "DAIMLERCHRYSLER JEEP
WRANGLER.RTM.," "JEEP WRANGLER.RTM.," or "5 PASSENGER JEEP
WRANGLER.RTM.."
[0052] The destination URL may represent the link the revenue
generator A 110A wishes a user A 120A to be directed to upon
clicking on the advertisement of the revenue generator A 110A, such
as the home page of the revenue generator A 110A. The bid amount
may represent a maximum amount the revenue generator A 110A may be
willing to pay each time a user A 120A may click on the
advertisement of the revenue generator A 110A or each time the
advertisement of the revenue generator A 110A may be shown to a
user A 120A.
[0053] The keywords may represent one or more search terms that the
revenue generator A 110A may wish to associate their advertisement
with. When a user A 120A searches for one of the listing's
keywords, the advertisement of the revenue generator A 110A may be
displayed on the search results page.
[0054] Alternatively or in addition, the service provider 130 may
implement a query suggestion system. A query suggestion system may
perform an analysis on the query of the user A 120A, or the query
determined from, or related to, the content of page, such as a web
page displayed to the user A 120A, to find additional queries that
may relate to the query of the user A 120A, or the query determined
from the content of a page. If additional queries are found,
advertisements with bids on any of the additional queries may be
displayed to the user A 120A in addition to the advertisements with
bids on the original query. Thus the user A 120A may click on an
advertisement of a revenue generator A 110A who did not bid on the
query the user A 120A searched for, or the query determined from
the content of a page, but a query matched to the query by a query
suggestion system. Some examples of query suggestion systems may
include King Kong, SPM, MOD, Units, or query suggestions derived
from a network of advertisers and users.
[0055] More detail regarding the aspects of query suggestions
systems, as well as their structure, function and operation, can be
found in commonly owned U.S. patent application Ser. No.
10/625,082, filed on Jul. 22, 2003, entitled, "TERM-BASED CONCEPT
MARKET"; U.S. patent application Ser. No. 11/295,166, filed on Dec.
5, 2005, entitled "SYSTEMS AND METHODS FOR MANAGING AND USING
MULTIPLE CONCEPT NETWORKS FOR ASSISTED SEARCH PROCESSING"; U.S.
patent application Ser. No. 10/797,586, filed on Mar. 9, 2004,
entitled "VECTOR ANALYSIS OF HISTOGRAMS FOR UNITS OF A CONCEPT
NETWORK IN SEARCH QUERY PROCESSING"; U.S. patent application Ser.
No. 10/797,614, filed on Mar. 9, 2004, entitled "SYSTEMS AND
METHODS FOR SEARCH PROCESSING USING SUPERUNITS"; U.S. Pat. No.
7,051,023, filed on Nov. 12, 2003, entitled "SYSTEMS AND METHODS
FOR GENERATING CONCEPT UNITS FROM SEARCH QUERIES," and U.S. Pat.
No. 6,876,997, filed on May 22, 2000, entitled "METHOD AND
APPARATUS FOR IDENTIFYING RELATED SEARCHES IN A DATABASE SEARCH
SYSTEM, all of which are hereby incorporated herein by reference in
their entirety. The systems and methods herein associated with
query suggestion systems analysis may be practiced in combination
with methods and systems described in the above-identified patent
applications incorporated by reference.
[0056] For example, a revenue generator A 110A, such as
DAIMLERCHRYSLER.RTM., may desire to target an online advertisement
for a CHRYSLER JEEP WRANGLER.RTM. to users 120A-N searching for the
keywords "JEEP.RTM.", "WRANGLER.RTM.", or "JEEP WRANGLER.RTM.".
DAIMLERCHRYSLER.RTM. may place a bid with the service provider 130
for the search keywords "JEEP.RTM.", "WRANGLER.RTM.", and "JEEP
WRANGLER.RTM." and may associate the online advertisement for a
CHRYSLER JEEP WRANGLER.RTM. with the keywords. The advertisement of
the revenue generator A 110A may be displayed when one of the users
120A-N searches for the keywords "JEEP.RTM.", "WRANGLER.RTM.", or
"JEEP WRANGLER.RTM.".
[0057] An advertisement may represent the data the revenue
generator A 110A wishes to be displayed to a user A 120A when the
user A 120A searches for one of the listing's keywords. An
advertisement may include a combination of the description and the
title. The ad groups may each contain several different
advertisements, which may be referred to as creatives. Each of the
individual advertisements in an ad group may be associated with the
same keywords. The advertisements may differ slightly in creative
aspects or may be targeted to different demographics of the users
120A-N.
[0058] There may be some instances where multiple revenue
generators 110A-N may have bid on the same search keyword. The
service provider 130 may serve to the users 120A-N the online
advertisements that the users 120A-N may be most likely to click
on. For example, the service provider 130 may include a relevancy
assessment to determine the relevancy of the multiple online
advertisements to the search keyword. The more relevant an
advertisement may be to the keyword the more likely it may be that
the user A 120A may click on the advertisement. The relevancy may
be determined by the service provider 130 or a third party
relevancy engine.
[0059] When one of the users 120A-N, such as the user A 120A,
interacts with the service provider 130, such as by searching for a
keyword, the service provider 130 may retain data describing the
interaction with the user A 120A. The stored data may include the
keyword searched for, the geographic location of the user A 120A,
and the date/time the user A 120A interacted with the service
provider 130. Further the data may include data describing the
number of prominent ads, or top ads displayed on the page to the
user. FIGS. 13 and 14 may show examples of top ads. The number of
top ads on a given page may be referred to as the "DUDE" state of
the page. The service provider 130 may retain the DUDE state of a
page or query when a user A 120A clicks on an advertisement. The
stored data may also generally include any data available to the
service provider 130 that may assist in describing the interaction
with the user A 120A, or describing the user A 120A.
[0060] The service provider 130 may also store data that indicates
whether an advertisement of one of the revenue generators 110A-N,
such as the revenue generator A 110A was displayed to the user A
120A, and whether the user A 120A clicked on the advertisement, or
generally any other data that may assist the revenue generators
110A-N in determining the effectiveness of their advertisements.
The data may also include data describing the rank of the
advertisement clicked on by the user A 120A. The rank may refer to
the order in which the advertisements are displayed on the page.
For example, the first displayed advertisement may have a rank of
"1," the second displayed advertisement may have a rank of "2," and
so on.
[0061] In some instances the advertisement may have been displayed
to the user A 120A as a result of a query suggestion from a query
suggestion system, or matching system, implemented by the service
provider 130. The query suggestion system may have suggested a
query matching the query of the user A 120A. The suggested query
may have had advertisements relevant to the query of the user A
120A and the relevant advertisement may have been displayed to the
user A 120A. In theses instances, the service provider 130 may
store the query that the service provider 130 matched to the query
of the user A 120A along with a unique identifier describing the
matching system that suggested the query, such as the name of the
matching system.
[0062] The users 120A-N may supply information relating to their
geographic location and/or other descriptive information upon their
initial interaction with the service provider 130. Alternatively or
in addition the service provider 130 may obtain the location of the
user A 120A based on the IP address of the user A 120A. The service
provider 130 may use a current date/time stamp to store the
date/time when the user A 120A interacted with the service provider
130.
[0063] The service provider 130 may generate reports based on the
data collected from the user interactions and communicate the
reports to the revenue generators 110A-N to assist the revenue
generators 110A-N in measuring the effectiveness of their online
advertising. The reports may indicate the number of times the users
120A-N searched for the keywords bid on by the revenue generators
110A-N, the number of times each advertisement of the ad groups of
the revenue generators 110A-N was displayed to the users 120A-N,
the number of times the users 120A-N clicked through on each
advertisement of the ad groups of the revenue generators 110A-N,
and/or the number of times a desired action was performed by the
users 120A-N after clicking through on an advertisement. The
reports may also generally indicate any data that may assist the
revenue generators 110A-N in measuring or managing the
effectiveness of their online advertising.
[0064] The reports may further include sub-reports that segment the
data into more specific categories, including the time intervals
when the interactions occurred, such as weeknights primetime,
weekends, etc., the demographics of the users 120A-N, such as men
ages 18-34, the location of the users 120A-N. The reports may also
generally include any other data categorization that may assist the
revenue generators 110A-N in determining the effectiveness of their
online advertising.
[0065] More detail regarding the aspects of auction-based systems,
as well as the structure, function and operation of the service
provider 130, as mentioned above, can be found in commonly owned
U.S. patent application Ser. No. 10/625,082, filed on Jul. 22,
2003, entitled, "TERM-BASED CONCEPT MARKET"; U.S. patent
application Ser. No. 10/625,000, file on Jul. 22, 2003, entitled,
"CONCEPT VALUATION IN A TERM-BASED CONCEPT MARKET" filed on Jul.
22, 2003; U.S. patent application Ser. No. 10/625,001, filed on
Jul. 22, 2003, entitled, "TERM-BASED CONCEPT INSTRUMENTS"; and U.S.
patent application Ser. No. 11/489,386, filed on Jul. 18, 2006,
entitled, "ARCHITECTURE FOR AN ADVERTISEMENT DELIVERY SYSTEM," all
of which are hereby incorporated herein by reference in their
entirety. The systems and methods herein associated with ad
campaign management may be practiced in combination with methods
and systems described in the above-identified patent applications
incorporated by reference.
[0066] FIG. 2 provides a simplified view of a network environment
200 implementing the system of FIG. 1 or other systems implementing
a system for building a data structure representing a network of
advertisers and users. Not all of the depicted components may be
required, however, and some implementations may include additional
components not shown in the figure. Variations in the arrangement
and type of the components may be made without departing from the
spirit or scope of the claims as set forth herein. Additional,
different or fewer components may be provided.
[0067] The network environment 200 may include one or more web
applications, standalone applications and mobile applications
210A-N, which may be collectively or individually referred to as
client applications for the revenue generators 110A-N. The system
200 may also include one or more web applications, standalone
applications, mobile applications 220A-N, which may collectively be
referred to as client applications for the users 120A-N, or
individually as a user client application. The system 200 may also
include a network 230, a network 235, the service provider server
240, a data store 245, a third party server 250, and an advertising
services server 260.
[0068] Some or all of the advertisement services server 260,
service provider server 240, and third-party server 250 may be in
communication with each other by way of network 235. The
advertisement services server 260, third-party server 250 and
service provider server 240 may each represent multiple linked
computing devices. Multiple distinct third party servers, such as
the third-party server 250, may be included in the network
environment 200. A portion or all of the advertisement services
server 260 and/or the third-party server 250 may be a part of the
service provider server 240.
[0069] The data store 245 may be operative to store data, such as
data relating to interactions with the users 120A-N. The data store
245 may include one or more relational databases or other data
stores that may be managed using various known database management
techniques, such as, for example, SQL and object-based techniques.
Alternatively or in addition the data store 245 may be implemented
using one or more of the magnetic, optical, solid state or tape
drives. The data store 245 may be in communication with the service
provider server 240. Alternatively or in addition the data store
245 may be in communication with the service provider server 240
through the network 235.
[0070] The networks 230, 235 may include wide area networks (WAN),
such as the internet, local area networks (LAN), campus area
networks, metropolitan area networks, or any other networks that
may allow for data communication. The network 230 may include the
Internet and may include all or part of network 235; network 235
may include all or part of network 230. The networks 230, 235 may
be divided into sub-networks. The sub-networks may allow access to
all of the other components connected to the networks 230, 235 in
the system 200, or the sub-networks may restrict access between the
components connected to the networks 230, 235. The network 235 may
be regarded as a public or private network connection and may
include, for example, a virtual private network or an encryption or
other security mechanism employed over the public Internet, or the
like.
[0071] The revenue generators 110A-N may use a web application
210A, standalone application 210B, or a mobile application 210N, or
any combination thereof, to communicate to the service provider
server 240, such as via the networks 230, 235. Similarly, the users
120A-N may use a web application 220A, a standalone application
220B, or a mobile application 220N to communicate to the service
provider server 240, via the networks 230, 235.
[0072] The service provider server 240 may communicate to the
revenue generators 110A-N via the networks 230, 235, through the
web applications, standalone applications or mobile applications
210A-N. The service provider server 240 may also communicate to the
users 120A-N via the networks 230, 235, through the web
applications, standalone applications or mobile applications
220A-N.
[0073] The web applications, standalone applications and mobile
applications 210A-N, 220A-N may be connected to the network 230 in
any configuration that supports data transfer. This may include a
data connection to the network 230 that may be wired or wireless.
Any of the web applications, standalone applications and mobile
applications 210A-N, 220A-N may individually be referred to as a
client application. The web applications 210A, 220A may run on any
platform that supports web content, such as a web browser or a
computer, a mobile phone, personal digital assistant (PDA), pager,
network-enabled television, digital video recorder, such as
TIVO.RTM., automobile and/or any appliance capable of data
communications.
[0074] The standalone applications 210B, 220B may run on a machine
that may have a processor, memory, a display, a user interface and
a communication interface. The processor may be operatively
connected to the memory, display and the interfaces and may perform
tasks at the request of the standalone applications 210B, 220B or
the underlying operating system. The memory may be capable of
storing data. The display may be operatively connected to the
memory and the processor and may be capable of displaying
information to the revenue generator B 110B or the user B 120B. The
user interface may be operatively connected to the memory, the
processor, and the display and may be capable of interacting with a
user A 120A or a revenue generator A 110A. The communication
interface may be operatively connected to the memory, and the
processor, and may be capable of communicating through the networks
230, 235 with the service provider server 240, third party server
250 and advertising services server 260. The standalone
applications 210B, 220B may be programmed in any programming
language that supports communication protocols. These languages may
include: SUN JAVA.RTM., C++, C#, ASP, SUN JAVASCRIPT.RTM.,
asynchronous SUN JAVASCRIPT.RTM., or ADOBE FLASH ACTIONSCRIPT.RTM.,
amongst others.
[0075] The mobile applications 210N, 220N may run on any mobile
device that may have a data connection. The data connection may be
a cellular connection, a wireless data connection, an internet
connection, an infra-red connection, a Bluetooth connection, or any
other connection capable of transmitting data.
[0076] The service provider server 240 may include one or more of
the following: an application server, a data store, such as the
data store 245, a database server, a middleware server, and an
advertising services server. The service provider server 240 may
co-exist on one machine or may be running in a distributed
configuration on one or more machines. The service provider server
240 may collectively be referred to as the server. The service
provider may implement a search engine marketing system and/or an
advertising campaign management system. The service provider server
240 may receive requests from the users 120A-N and the revenue
generators 110A-N and may serve pages to the users 120A-N and the
revenue generators 110A-N based on their requests.
[0077] The third party server 250 may include one or more of the
following: an application server, a data source, such as a database
server, a middleware server, and an advertising services server.
The third party server may implement a relevancy engine, a context
matching engine, or any other third party application that may be
used in a search engine marketing system and/or an advertising
campaign management system. The third party server 250 may co-exist
on one machine or may be running in a distributed configuration on
one or more machines. The third party server 250 may receive
requests from the users 120A-N and the revenue generators 110A-N
and may serve pages to the users 120A-N and the revenue generators
110A-N based on their requests.
[0078] The advertising services server 260 may provide a platform
for the inclusion of advertisements in pages, such as web pages.
The advertisement services server 260 may be used for providing
advertisements that may be displayed to the users 120A-N. The
advertising services server 260 may implement a search engine
marketing system and/or an advertising campaign management
system
[0079] The service provider server 240, the third party server 250
and the advertising services server 260 may be one or more
computing devices of various kinds, such as the computing device in
FIG. 15. Such computing devices may generally include any device
that may be configured to perform computation and that may be
capable of sending and receiving data communications by way of one
or more wired and/or wireless communication interfaces. Such
devices may be configured to communicate in accordance with any of
a variety of network protocols, including but not limited to
protocols within the Transmission Control Protocol/Internet
Protocol (TCP/IP) protocol suite. For example, the web applications
210A, 210A may employ HTTP to request information, such as a web
page, from a web server, which may be a process executing on the
service provider server 240 or the third-party server 250.
[0080] There may be several configurations of database servers,
such as the data store 245, application servers, middleware servers
and advertising services servers included in the service provider
server 240, or the third party server 250. Database servers may
include MICROSOFT SQL SERVER.RTM., ORACLE.RTM., IBM DB2.RTM. or any
other database software, relational or otherwise. The application
server may be APACHE TOMCAT.RTM., MICROSOFT IIS.RTM., ADOBE
COLDFUSION.RTM., YAPACHE.RTM. or any other application server that
supports communication protocols. The middleware server may be any
middleware that connects software components or applications. The
middleware server may be a relevancy engine, a context matching
engine, or any other middleware that may be used in a search engine
marketing system and/or an advertising campaign management
system.
[0081] The application server on the service provider server 240 or
the third party server 250 may serve pages, such as web pages to
the users 120A-N and the revenue generators 110A-N. The advertising
services server may provide a platform for the inclusion of
advertisements in pages, such as web pages. The advertising
services server 260 may also exist independent of the service
provider server 240 and the third party server 250. The
advertisement services server 260 may be used for providing
advertisements that may be displayed to users 120A-N on pages, such
as web pages.
[0082] The networks 230, 235 may be configured to couple one
computing device to another computing device to enable
communication of data between the devices. The networks 230, 235
may generally be enabled to employ any form of machine-readable
media for communicating information from one device to another.
Each of networks 230, 235 may include one or more of a wireless
network, a wired network, a local area network (LAN), a wide area
network (WAN), a direct connection such as through a Universal
Serial Bus (USB) port, and the like, and may include the set of
interconnected networks that make up the Internet. The networks
230, 235 may include any communication method by which information
may travel between computing devices.
[0083] FIG. 3 illustrates a system 300 for building a data
structure representing a network of advertisers and users. The
system 300 may include an ad serving system 310, a graph component
320, a service provider server 240, a data store 245, and a network
235. The ad serving system 310 may be implemented by the service
provider server 240, the ad services server 260, or the third party
server 250. The ad serving system 310 may be an auction-based ad
serving system. The ad serving system 310 may include an ad data
store 318, a sponsored search server 312, a content match server
316, and a redirect server 314. The graph component 320 may be
implemented by the service provider server 240, the ad services
server 260, or the third party server 250. The graph component 320
may include a graph processor 322, a graph analyzer 324, and a
graph data store 326. The graph component 320 may exist on one
machine or may be running in a distributed configuration on one or
more machines. The one or more machines of the graph component 320
may be one or more computing devices of various kinds, such as the
computing device in FIG. 15. Not all of the depicted components may
be required, however, and some implementations may include
additional components not shown in the figure. Variations in the
arrangement and type of the components may be made without
departing from the spirit or scope of the claims as set forth
herein. Additional, different or fewer components may be
provided.
[0084] The ad data store 318 may be operative to store data, such
as advertisement listings. The ad data store 318 may include one or
more relational databases or other data stores that may be managed
using various known database management techniques, such as, for
example, SQL and object-based techniques. Alternatively or in
addition the ad data store 318 may be implemented using one or more
of the magnetic, optical, solid state or tape drives.
[0085] The sponsored search server 312 may be operative to process
sponsored search listing requests from the client applications
210A-N, received via the service provider server 240 or the graph
component 320. When a request for a sponsored search listing comes
from service provider server 240 or the graph component 320, the
sponsored search server 312 may query the ad data store 318 for any
advertisements, matching the search terms specified in the request.
If matching ad listings are available in the ad data store 318, the
sponsored search server 312 may return the retrieved data to the
service provider server 240. The service provider server 240 may
then serve the ad listings, such as sponsored listings, to the
client applications 210A-N. The advertisements may be displayed in
descending order based on the bid value for the given search terms
whereby matching ads with the highest bids are displayed first
followed by the lower bid advertisements. Alternatively or in
addition the advertisements may be displayed in the order based on
the relevancy of the advertisements to the search terms. The
relevancy may be determined by a relevancy engine implemented on
the service provider server 240 or the third party server 250.
[0086] The content match server 316 may operate in a similar
manner. The content match server 316 may be operative to process
content match listing requests from the service provider server 240
or the graph component 320. When a request for a content match
listing comes from the service provider server 240 or the graph
component 320, the content match server 316 may query the ad data
store 318 for any advertisements matching the search terms
specified in the request. If matching ad listings are available in
the ad data store 318, the content match server 316 may return the
data the service provider server 240. The service provider server
240 may then serve the advertisements to the client applications
210A-N. The advertisements may be displayed in descending order
based on the bid value for the given search terms whereby matching
ads with the highest bids are displayed first followed by the lower
bid advertisements. Alternatively or in addition the advertisements
may be displayed in the order based on the relevancy of the
advertisements to the search terms. The relevancy may be determined
by a relevancy engine implemented on the service provider server
240 or the third party server 250.
[0087] The graph component 320 may be operative to build, store,
and analyze data representing a graph through the graph processor
322, the graph analyzer 324, and the graph data store 326. The
graph may be a data representation of a network of advertisers and
users through relationships between advertisements and queries. A
query may refer to the set of terms searched for by one of the
users 120A-N, or a set of terms that may be related to the content
on a page displayed to one of the users 120A-N.
[0088] The graph data store 326 may be operative to store data,
such as data describing a network of advertisers and users, or
advertisements and queries. The graph data store 326 may include
one or more relational databases or other data stores that may be
managed using various known database management techniques, such
as, for example, SQL and object-based techniques. Alternatively or
in addition the graph data store 326 may be implemented using one
or more of the magnetic, optical, solid state or tape drives.
[0089] The graph processor 322 may be operative to process
historical data, such as historical click data to generate data
describing a network of advertisers and users, as illustrated below
in FIG. 4. The network of advertisers and users may be represented
by data describing relationships between advertisements and
queries.
[0090] The graph processor 322 may store the graph data in the
graph data store 326. The graph processor 322 may retrieve the
historical data from the data store 245 to generate the graph data.
The graph processor 322 may be in communication with the data store
245, or may access the data store 245 via the service provider
server 240.
[0091] The graph processor 322 may build the graph by processing
the historical data. The historical data may be processed to build
link data describing the relationships between the queries, such as
search queries of the users 120A-N and/or queries, or a set of
terms, related to the content on a page displayed to the user
120A-N, and the advertisements displayed as a result of the
queries. The links may be weighted by a metric describing the
effectiveness of the advertisement, such as data related to user
click throughs, conversions, or any other metric measuring the
effectiveness of the online advertisements. The graph may be
independent of the language and other regional characteristics of
the underlying data. The graph processor 322 may be capable of
generating the graph by using any of the aforementioned metrics
measuring the effectiveness of online advertisements.
[0092] Alternatively or in addition the graph processor 322 may
only generate a link between a query and an advertisement if one of
the users 120A-N clicked through on the advertisement. Therefore if
an advertisement was displayed to the users 120A-N for a particular
query and none of the users 120A-N clicked on the advertisement
during the period of time represented by the historical data then
the graph processor 322 may not generate a link for the
advertisement/query pair.
[0093] The graph processor 322 may re-process the historical data
to build a new graph at set intervals, such as daily, weekly,
monthly, or any other period that may increase the accuracy of the
graph's representation of the network. The graph data store 326 may
store every build of the graph, identifying each individual build
by the date/time the build occurred.
[0094] The graph analyzer 324 may be operative to analyze the
stored graph data to perform a specified task, such as supplying
suggested search terms related to the terms searched for by one of
the users 120A-N, supplying advertisements related to the terms
search for, or any other task that may be accomplished by analyzing
the graph data. The graph analyzer 324 may analyze the graph in
real time, such as when a search term is received from the service
provider.
[0095] Alternatively or in addition, the graph analyzer 324 may
pre-process the graph data to generate a separate data structure.
The data structure may be hashmap linking each query to relevant
queries and advertisements. Large scale implementations of the
network may require offline pre-processing of the graph data.
[0096] The graph analyzer 324 may be operative to analyze the graph
to increase the depth and competitiveness of keywords using the
graph. The graph analyzer 324 may be operative to analyze the graph
to generate keyword suggestions which may be queried at
advertisement serving time, presented to advertisers during
campaign management and/or added to augment advertisements to be
served by the service provider server 240.
[0097] The graph analyzer 324 may be operative to analyze the graph
to evaluate the quality (relevance, value) of keyword suggestions
and other matching techniques in the first and higher orders. The
graph analyzer 324 may be operative to analyze the graph to
determine high performing suggestions and to explore unknown or low
value suggestions scheduled by some measure based on relevance. The
graph analyzer 324 may be operative to analyze the graph to
estimate the relative quality of advertisements.
[0098] The graph analyzer 324 may be operative to analyze the graph
to capture the semantic knowledge gap between raw user queries
(often syntactically different) and underlying user intent behind
the queries. The graph analyzer 324 may use the semantic knowledge
gap to generate keyword suggestions. For example, each query may
describe an intent and/or need of a user A 120A in the form of a
set of keywords. The user intent and/or need may not be accurately
represented by the queries since the queries may only partially
capture the semantic intent of the user A 120A.
[0099] The graph analyzer 324 may associate the queries of the user
A 120A with relevant queries of the other users 120B-N per the
calculations below. The graph analyzer 324 may organize the queries
into groups. The membership of a query q in a group may be
determined by the number of relevant queries that the query q
shares with other queries that may be a member of the group.
Membership to a group may be partial. For example the query q may
belong to group X at 70% and to group Y at 30%. Membership to a
group may be determined by any data clustering algorithm, such as
k-means clustering, QT clustering, or Fuzzy c-means clustering.
[0100] Once the allocation of queries to each group has been
completed the salient queries in each group may be used to describe
the semantic intent of the user A 120A. The salient query of each
group may also be determined by utilizing data clustering
algorithms, such as k-means clustering, QT clustering, or Fuzzy
c-means clustering. For example, a user query such as "mp3 player"
may have relevant queries of "ipod".RTM. and "noise-canceling
headphones." The query "ipod".RTM. may belong to the query group
described by "portable music players" while the query
"noise-canceling headphones may belong to the query group described
by "music players accessories."
[0101] The graph analyzer 324 may then determine the relationship
value between the user query and the salient queries of each group.
The groups that are found to be closely related to the user query
may capture the semantic intent of the user A 120. The user query
may then be matched with queries and advertisements associated with
these groups.
[0102] Alternatively or in addition to the groups may be organized
by both queries and advertisements that the users 120A-N clicked
on. The salient representative may be either queries or
advertisements.
[0103] The graph analyzer 324 may be operative to analyze each
successive build of the graph to determine advertiser and/or user
changes, such as changes in advertiser participation, advertiser
intent, advertiser valuation and spend, user behavior, demographics
and mix, aggregate user intent and mix, and search usage. The graph
analyzer 324 may be operative to combine various builds of the
graph across markets defined by language and other regional
characteristics.
[0104] The graph analyzer 324 may be operative to analyze
successive builds of the graph to capture temporal shifts in user
intent, advertiser intent and/or context. The graph analyzer 324
may use the captured temporal shift to identify a corresponding
shift in the semantic knowledge in the form of keyword suggestions.
The keyword suggestions may implicitly capture language seasonal
patterns, and progress of human knowledge representation in the
form of language.
[0105] The graph analyzer 324 may be operative to identify both
significantly related and unrelated sub-networks within the network
represented by the graph. The sub-networks may be identified based
on keyword semantic affinities, advertiser online spend, and/or
historical performance based on user interactions and revenue
generated by the advertisements. In addition, the graph analyzer
324 may be operative to clustering groups of related nodes. For
example, the relationship or proximity of queries and
advertisements may be determined, as demonstrated below. The
queries that are determined to be closely related may be grouped
into a node. The advertisements related to those queries may also
be grouped into a node. These nodes may provide a higher level
perspective of the network. Alternatively or in addition
advertisements of a common advertiser may be grouped together. This
network may be used to determine information, such as demographics,
about the users 120A-N interested in a certain advertiser,
regardless of the specific advertisement.
[0106] FIG. 4 is a graph 400 illustrating an example of a network
of advertisers and users built by the system 300 of FIG. 3 or other
systems for generating a network of advertisers and users. The
graph 400 may be a bipartite graph. A bipartite graph may be a
graph containing two types of nodes or points. In a bipartite graph
no node may be linked to another node of the same type.
[0107] In the case of the graph 400, the node types may be query
nodes and advertisements nodes. The query nodes may represent
queries performed by the users 120A-N as represented in the
historical data, queries related to content on a page displayed to
the users 120A-N, and/or any other set of terms that may be matched
to an advertisement. The query nodes may represent the interest of
the users 120A-N as demonstrated through the search queries. The
advertisement nodes may represent the advertisements that may have
been displayed to, or clicked on, by the users 120A-N as a result
of the queries. The advertisement nodes may represent the revenue
generators 110A-N, such as advertisers, or more particularly the
advertisement nodes may represent the intent of the advertisers.
The advertisers' intent may be demonstrated through the queries the
advertisements may be linked to and therefore the queries the
advertisers' may have previously bid on.
[0108] A query node may be linked to an advertisement node if one
of the users 120A-N, such as the user A 120A searches for the query
and the advertisement is displayed to the user A 120A as a result
of the query. Alternatively or in addition a query node may be
linked to an advertisement node if an advertisement is displayed to
the user A 120A as a result of a query related to the content of a
page, such as a page displayed to the user A 120A. In the graph
400, the users 120A-N may have searched for Query1, Query2, and
Query3. When the users 120A-N searched for Query1, Ad1, Ad2, and
Ad3 may have been displayed. When the users 120A-N searched for
Query2, Ad3, Ad4, and Ad5 may have been displayed. When the users
120A-N searched for Query3, Ad3, Ad5, and Ad6 may have been
displayed.
[0109] Once a link between a query and an advertisement is
established, the query may be weighted, or quantified, based on a
metric relating to the relationship between the advertisement and
the query. For example, the link may be weighted based on the click
through rate of the advertisement for the particular query. The
click through rate for the link may be only account for the
click-throughs attributed to when the advertisement is displayed as
a result of the query represented by the query node. The click
through rate may be calculated over the period of time T
represented by the historical data, such as the previous day, week,
month, year, or any other time period. The weights can be seen in
the graph 400 as values on the lines representing the links. The
click through rate for a particular advertisement/query pair may be
0.0 if none of the users 120A-N clicked on the advertisement.
[0110] FIG. 5 is a flowchart illustrating the operations of the
system of FIG. 3, or other systems for building a data structure
representing a network of advertisers and users. At block 505 the
graph processor 322 may identify historical data, such as
historical user interaction data, historical user click data,
historical ad display data, historical ad performance data, or
generally any data relating to queries or the display of the
resulting advertisements. The graph processor 322 may retrieve the
historical data directly from the data store 245 or via the service
provider 240. The historical data may represent all of the
historical data for a given time period T, such as the previous
day, month, year, or any other determinable time period. At block
510 the graph processor 322 may identify all of the individual
queries in the historical data. The queries may represent a set of
search terms or queries searched for by the users 120A-N during the
time period T, such as through a search engine provided by the
service provider 130. There may be more than one instance of a
query if it was searched for more than once by the users 120A-N;
however, the underlying data describing the particular user
interaction may differ, and thus each query is processed
individually. Alternatively or in addition the queries may
represent a set of terms related to the content of a page, such as
a page displayed to the users 120A-N during the time period T. The
page may have been served to the users 120A-N by the service
provider 130 and/or a service provider partner.
[0111] At block 515 the graph processor 322 may retrieve the first
query, q, from the identified user queries. At block 520 the graph
processor 322 may identify the advertisements displayed to the user
A 120A when the user A 120A searched for q, or the advertisements
displayed to the user A 120A as a result of content relating to q.
At block 525 the graph processor 322 may retrieve the first
advertisement, a, displayed for query q. At block 530 the graph
processor 322 may process the data associated with this particular
pairing of the query q and the advertisement a to generate raw
context data. The operations of processing the data associated with
the query/advertisement pair may be demonstrated in detail in FIG.
6.
[0112] At block 535 the graph processor 322 may determine if the
raw context data of the query/advertisement pairing is unique. To
determine if the raw context data is unique the graph processor 322
may compare the raw context data with existing raw context data in
the graph data store 326. If the raw context data is unique the
system 300 may move to block 545 to store the raw context data of
the query/advertisement pair. If the raw context data is not unique
the system 300 may move to block 550. The raw context data of
query/advertisement pair may be described in FIG. 6.
[0113] At block 545 the graph processor 322 may store the raw
context data representing the query/ad pair in the graph data store
326. At block 550 the graph processor 322 may determine whether
there are more advertisements which were displayed as a result of
the query q. If there are more advertisements, the system 300 may
move to block 555. At block 555 the graph processor 322 may
retrieve the next advertisement for the query q. The system 300 may
then return to block 530 and repeat the operations for the
advertisement. Once the system 300 has cycled through all of the
advertisements for the query the system 300 may move to block 560.
At block 560 the graph processor 322 may determine if there are
remaining queries in the historical data. If there are no remaining
queries then the system 300 may move to block 370. If there are
more queries in the historical data then the system 300 may move to
block 565. At block 565 the graph processor 322 may retrieve the
next query. The system 300 may the return to block 520 and repeat
the operations for the query.
[0114] At block 570 the system 300 may generate a link for each
unique query/ad pair. The operations of generating the links for
the query/ad pairs may be elaborated in FIG. 7. The query/ad links
may be generated at a higher level of granularity than the raw
context data of a query/ad pair. Thus there may be one query/ad
link representing the raw context data of several query/ad
pairs.
[0115] FIG. 6 is a flowchart illustrating the operations of
identifying the raw context data representing a query/advertisement
pairing in the system of FIG. 3, or other systems for building a
data structure representing a network of advertisers and users. At
block 605 the graph processor 322 may identify a query q, such as
the first query selected from the historical dataset in block 515
of FIG. 5. At block 610 the graph processor 322 may identify the
advertisements that may have been displayed to one of the users
120A-N, such as the user A 120A, after the user A 120A searched for
the query q, or the advertisements displayed to one of the users
120A-N as a result of the content q may have been related to.
[0116] At block 615 the graph processor 322 may identify the DUDE
state D of the query. The DUDE state may refer to the number of
advertisements that may have been prominently displayed to the user
A 120A, such as top advertisements. Top advertisements are shown in
more detail in FIGS. 13 and 14 below. Since the DUDE state
indicates the number of prominent advertisements displayed to the
user A 120A, the higher the value of the DUDE state the more likely
that the user A 120A may have clicked on one of the advertisements.
Therefore the DUDE state may need to be accounted for in order to
accurately determine the effectiveness of the advertisements of the
revenue generators 110A-N. The value of the DUDE state for the
query q may be obtained from the historical data.
[0117] At block 620 the graph processor 322 may retrieve the first
advertisement a displayed for the query q. At block 625 the graph
processor 322 may determine whether the advertisement a was
displayed as a result of a query suggestion from a matching system.
As previously mentioned, the service provider server 240 may
implement one or more matching systems that may suggest queries
that may relate to the query of the user A 120A. Advertisements may
be retrieved from the ad data store 318 for the original query of
the user A 120A and any queries suggested by the matching systems.
The most relevant ads may be displayed to the user A 120A.
Alternatively or in addition the ads with the highest bids, for the
original query or any suggested queries, may be displayed to the
user A 120A, or any combination of the bid and the relevance. Data
indicating whether the advertisement a was displayed as a result of
a query suggested by a matching system may be obtained from the
historical data.
[0118] If the advertisement a was displayed as a result of a query
suggestion of a matching system, the system 300 may move to block
630. At block 630 the graph processor 322 may identify the query q'
that was suggested by the matching system. The graph processor 322
may also identify the matching system M that suggested the query
q'. The suggested query q' and the matching system M may be
obtained from the historical data. Storing the matching system
identification M may allow the system 300 to attribute the value of
a link to the matching system that generated the suggestion. If the
advertisement a was not displayed as a result of a query suggestion
of a matching system, the system 300 may move to block 635.
[0119] At block 635 the graph processor 322 may calculate the
average rank r of the advertisement a when it was displayed as a
result of any instance of the query q. The rank of an advertisement
may be the order in which it was displayed on the page to the user
A 120A. For example, if the advertisement was the first
advertisement displayed it may have a rank of 1, the second ad a
rank of 2, and so on. FIGS. 16 and 17 below may elaborate on the
rank of an advertisement. The graph processor 322 may calculate the
sum of the each rank of the advertisement a when it was displayed
as a result of any instances of the query q, regardless of whether
a was displayed due to a matching system. The sum may then be
divided by the number of times the advertisement a was displayed as
a result of the query q to calculate the average rank r. The data
for the average rank calculation may be obtained from the
historical data.
[0120] At block 640 the graph processor 322 may calculate the total
number of clicks C, the total number of impressions I and/or the
total number of conversions V for the advertisement a when it was
displayed on a results page with a DUDE state D as a result of the
query q. The graph processor 322 may calculate the total number of
impressions I by retrieving from the historical data the number of
times the advertisement a was displayed to the users 120A-N as a
result of the query q, on a page with a DUDE state of D, regardless
of whether a was displayed because of a suggested query. The graph
processor 322 may calculate the total number of clicks C by
retrieving from the historical data the number of times one of the
users 120A-N clicked on the advertisement a on a search results
page with a DUDE state D after searching for the query q,
regardless of whether a was displayed because of to a suggested
query. The graph processor 322 may calculate the total number of
conversions V by retrieving from the historical data the number of
times one of the users 120A-N performed a desired action on a web
site of one of the revenue generators 110A-N, such as making a
purchase, after searching for the query q and clicking on the
advertisement a on a search results page with a DUDE state D,
regardless of whether a was displayed due to a suggested query.
[0121] At block 650 the graph processor 322 may identify the
average cost per click ppc for the advertisement a when it was
retrieved by query q during the time period T. The graph processor
322 may calculate the average cost per click by calculating the sum
of the cost for each click on the advertisement a when it was
retrieved by query q in the historical data and dividing the sum by
the total number of clicks on the advertisement a when it was
retrieved by query q.
[0122] At block 655 the graph processor may aggregate the
identified raw context data relating to the advertisement a and
query q pair. The raw context data may include C the total clicks
on ad listing a for query q, I the total impressions of ad listing
a for query q, V the total conversions attributed to a click on ad
listing a for query q, M (if any) the match type that retrieved ad
listing a for query q, q' (if any) the actual bidded term
responsible for the display of the ad listing a, D the DUDE state
at the time of serving, r the average rank of the ad a when
retrieved by query q, and ppc the average cost the revenue
generator responsible for advertisement a pays per click when a is
retrieved by q. The total number of clicks C, impressions I, and
conversions V for a q/a pair may be calculated by taking a
summation of the individual values for each DUDE state D that may
exist for the q/a pair.
[0123] At block 660 the graph processor 322 may determine whether
the aggregated raw context data relating to the q/a pair is unique.
The graph processor 322 may search the graph data store 326 for an
instance of a query/ad pair with the same raw context data as the
q/a pair. The q/a pair may be unique if no other query/ad pair
exists in the graph data store 326 with the same query q,
advertisement a, match type M, suggested query q', and DUDE state
Dr. If no query/ad pair is found in the graph data store 326
matching the raw context data of the q/a pair then the q/a pair may
be unique. If the q/a pair is unique the system 300 may move to
block 665. At block 665 the graph processor 322 may store the raw
context q/a data in the graph data store 322. If the q/a pair is
not unique, the system 300 may move to block 670.
[0124] At block 670 the graph processor 322 may determine if there
are additional advertisements which were displayed when the user A
120A searched for the query q. If additional advertisements exist,
the system 300 may move to block 675. At block 675 the graph
processor 322 may select the next advertisement. The system 300 may
then return to block 625 and repeat the operations for the selected
advertisement. The graph processor 322 may cycle through the
operations for each of the advertisements displayed to the user A
120A after searching for the query q.
[0125] FIG. 7 is a flow chart illustrating the operations of
building a link between each unique query and advertisement in the
system of FIG. 3, or other systems for building a data structure
representing a network of advertisers and users. At block 705 the
graph processor 322 may identify all of the query/ad raw context
data stored in the graph data store 326. At block 710 the graph
processor 322 may select the first raw context query/ad data,
q/a.
[0126] At block 715 the graph processor 322 may determine whether a
link exists in the graph data store 326 for q and a. A link between
a q and a may be referred to as (q, a). The links may represent the
framework for the query/advertisement graph and may be stored in a
separate data structure from the query/ad pair data, such as a
separate database table. There may only be one link for a given q
and an a, while there may be several raw context data entries for a
given q and a, such as raw context data with different match types,
suggested queries, and/or DUDE states. Thus, the graph processor
322 may search the graph data store 326 to determine if a link from
the query q to the ad listing a exists. If a link does not exist,
the system 300 may move to block 720.
[0127] At block 720 the graph processor 322 may generate a link (q,
a) between the query q and the advertisement a. The link may
include an association between the query q and the advertisement a,
such as a data entry linking the two. Visually the link may
represent an edge in the bipartite graph.
[0128] At block 725 the graph processor 322 may calculate the
weight of the (q, a) link. The weight may be thought of as the
strength of the association between the query q and the
advertisement a. The weight may also represent the relevance of the
advertisement a to the query q. The weight may be represented as
w(q, a). Since the DUDE state may have an impact on the
effectiveness metrics the weights may often be calculated for each
individual DUDE state D of a query q. The weight of (q, a) for a
particular DUDE state D may be represented as w(q, a, D).
[0129] The weight, or relevance and/or utility measure, may be
represented by several different metrics, such as clicked or not
clicked, total clicks, un-normalized click through rate, position
normalized click through rate, or generally any metric that may
indicate the relevance or utility of q to a. Some examples of
utility may include whether a conversion occurred or not, total
conversions, un-normalized conversion rate, or position normalized
conversion rate. A q may only have one relevance measure w(q,a) for
any given a; in the position normalized case a q may have only one
DUDE state during the time period T
[0130] The value of clicked or not clicked weight may be 1 if a was
clicked at least once as a result of q with a DUDE state of D over
the period of time T, or 0 otherwise. The graph processor 322 may
determine that a was clicked at least once if the total clicks, C,
for (q, a) is greater than 0. The total clicks C may be determined
from data stored in the graph data store 326. A weight of total
clicks may be the total number of clicks for (q, a). The total
clicks may be determined from data in the graph data store 326. A
weight of total clicks for a given DUDE state may be determined
by:
w(q,a,D)=Clicks(q,a,D).
[0131] A weight of an un-normalized click through rate may be the
total clicks C for (q, a) divided by the total number of
impressions I for (q, a). The total clicks and total impressions
(q, a) may be determined from data in the graph data store 326. The
weight as an un-normalized click through rate for a particular DUDE
state D of (q, a) may be determined by:
w ( q , a , D ) = Clicks ( q , a , D ) I ( q , a , D ) .
##EQU00001##
[0132] A weight of a position normalized click through rate of
(q,a) for DUDE state D, also referred to as the Clicks over
Expected Clicks (COEC) may be determined by:
w ( q , a , D ) = COEC ( q , a , D ) = Clicks ( q , a , D ) I ( q ,
a , D ) refCTR ( D , r ) ##EQU00002##
where r may be the average rank associated with the (q, a). The
refCTR may be a reference click through rate curve for the DUDE
state D and average rank r of (q, a) averaged over all ads stored
in the graph data store 326. Since the rank r stored in the graph
data store 326 is an average rank, the average ranks may be rounded
to the nearest integer. The average rank for the (q, a) may be
retrieved from the graph data store 326. The refCTR may be
calculated by:
refCTR ( D , r ) = a .di-elect cons. A C ( D , r , a ) a .di-elect
cons. A I ( D , r , a ) . ##EQU00003##
[0133] Alternatively or in addition, two weights may be calculated,
a weight based on clicks, w1(q,a,D)=Clicks(q,a,D), and a weight
based on conversions, w2(q, a, D)=Conversions(q, a, D). The two
weights, w1 and w2, may be combined to determine the weight w. The
weights may be combined by the following calculation:
w ( q , a , D ) = k * w 1 ( q , a , D ) I ( q , a , D ) refCTR ( D
, r ) + ( 1 - k ) * w 2 ( q , a , D ) . ##EQU00004##
In this case k may be a system constant, such as 0.1, 1, 10, or any
value.
[0134] Alternatively or in addition the weight may be scaled by a
factor, referred to as the inverse advertiser frequency (IAF). An
individual IAF may be determined for each advertisement. The IAF
may indicate the overall importance of an advertisement a to a
query q as compared to other advertisements. The IAF may be
computed from the log data or be assigned through some other
definition or heuristic process. An advertisement a that is
associated with a very large number of queries may not provide a
strong indication of an association between a and any one of the
queries. In this instance the IAF may be a very small value. If an
advertisement is associated with a very small number of queries,
the advertisement may be specialized to address solely this narrow
set of queries. In this instance the IAF may be a very large value.
After the IAF is determined it may be used to adjust, or scale, the
weight through the following calculation:
w(q,a,D)=w(q,a,D)*IAF.
[0135] Alternatively or in addition, the weight may be adjusted to
account for the reliability of the weight. The reliability of a
weight may depend upon the number of values, such as clicks, that
contribute to the weight. For example, weights derived from only a
few clicks may be unreliable. In order to account for the
reliability of a weight the weight may be adjusted to incorporate a
measure of reliability in its estimate. In the case of weights
derived from clicks, a reliability factor rf may be determined. The
rf may be equal to 1.0 if there are more than 100 clicks,
indicating a reliable weight or value. If there are fewer than 100
clicks the rf may be a value between 0 and 1.0. As the number of
clicks approaches 0, the rf may also approach 0. In one instance
the rf may be linearly related to the number of clicks below 100.
For example, if there are 50 clicks, then the rf may be 0.5, and if
there are 25 clicks the rf may be 0.25. Once the rf is determined
it may be applied to the weight between q and a by the following
calculation:
w(q,a,D)=w(q,a,D)*rf.
[0136] Alternatively or in addition the reliability factor rf may
also be determined from the conversion rate associated with the
query advertisement pair. A high conversion rate may indicate a
strong link between the query q and the advertisement a. Query/ad
pairs with higher conversion rates may be considered more
significant than those with lower conversion rates. The conversion
rate may be used to calculate the reliability factor separate from,
or in addition to, using the clicks. For example, a query/ad pair
with a low number of clicks may still be reliable if it has a high
conversion rate. Once the rf factor due to conversions is
determined it may be applied to the following equation to adjust
the weight:
w(q,a,D)=w(q,a,D)*rf.
[0137] After calculating a weight for the link (q, a) and/or a
weight for each DUDE state D that exists for (q, a), the system 300
may move to block 730. At block 730 the graph processor 322 may add
the weights to the data entry representing the link (q, a). At
block 735 the graph processor 322 may store the data representing
the link (q, a), including the weights, in the graph data store
326. If the query/ad pair was not unique in block 715, the system
300 may move to block 740.
[0138] At block 740 the graph processor 322 may determine whether
there area any additional query/ad pairs. If there are additional
query/ad pairs the system 300 may move to block 745. At block 745
the graph processor 322 may select the next query/ad pair. The
system 300 may then move to block 715 and repeat the operations for
the query ad/pair. The graph processor 322 may repeat the
operations for each query/ad pair identified in the graph data
store 326.
[0139] FIG. 8 is a flow chart illustrating the operations of using
a network of advertisers and users built by the system of FIG. 3,
or other systems for building a data structure representing a
network of advertisers and users, to suggest queries related to a
query q. At block 810 the graph component 320 may receive a query
q, such as from the service provider server 240. The query q may
have been searched for by one of the users 120A-N, such as the user
A 120A. Links between the query q and advertisements may exist in
the graph data store 326. The query q may be communicated to the
graph analyzer 324.
[0140] At block 820 the graph analyzer 324 may determine whether
the graph data representing the query/advertisement graph was
pre-processed. The graph may be pre-processed offline to build all
of the outputs that the graph may be utilized to generate, such as
queries related to a query q. The outputs for a given query may be
stored in a hashmap to enable a quick and efficient lookup of the
data. In very large implementations of the system 300 the
processing delay may require calculating any potential outputs
offline. The steps that follow may be performed offline if the
graph data is pre-processed.
[0141] If the graph data was not pre-processed the system 300 may
move to block 830. At block 830 the graph analyzer 324 may identify
all queries Q and all advertisements A which are a part of a link
in the graph data store 326. At block 840 the graph analyzer 324
may calculate a relevance value R for each query in Q. The
relevance may indicate how relevant each query in Q is to the query
q received from the service provider server 240. For a given query
q' in Q, the relevance value R for (q, q') may be determined by the
following equation:
R ( q , q ' ) = a .di-elect cons. A ( w ( q , a , D q ) - W _ q ) (
w ( q ' , a , D q ' ) - W _ q ' ) a .di-elect cons. A q ( w ( q , a
, D q ) - W _ q ) 2 a .di-elect cons. A q ' ( w ( q ' , a , D q ' )
- W _ q ' ) 2 . ##EQU00005##
In R(q, q'), Dq may be the DUDE state of (q, a) and Dq' may be the
DUDE state of (q', a). The graph analyzer 324 may obtain the
weights, w(q, a, D), from the graph data store 326. W.sub.q may be
the weight value for the position normalized click through rate as
calculated by:
w ( q , a , D ) = COEC ( q , a , D ) = Clicks ( q , a , D ) I ( q ,
a , D ) refCTR ( D , r ) . ##EQU00006##
Alternatively or in addition W.sub.q may be calculated by:
W _ ( a ) = q .di-elect cons. Q a , D Clicks ( q , a ) COEC ( q , a
, D ) q .di-elect cons. Q a , D Clicks ( q , a ) . ##EQU00007##
[0142] Alternatively or in addition, in some situations, such as
when the distribution scales of the weights are relatively equal,
the relevance value R of (q, q') may be determined by:
R ( q , q ' ) = a .di-elect cons. A ( w ( q , a , D q ) ) ( w ( q '
, a , D q ' ) ) a .di-elect cons. A q ( w ( q , a , D q ) ) 2 a
.di-elect cons. A q ' ( w ( q ' , a , D q ' ) ) 2 .
##EQU00008##
[0143] The value of R(q, q') may be further enhanced by including
an overlap factor OF. The overlap factor OF may describe the number
of advertisements the queries q and q' may have in common and/or
the amount of search traffic the queries q and q' may have in
common. For example a query q may link to 10 advertisements, a
query q' may link to 5 advertisements, and the queries q and q' may
share 3 advertisements in common. The value of R(q, q') may then be
adjusted by the following calculation:
R(q,q')=R(q,q')*OF
[0144] After calculating R(q, q') for every q' in Q, the system 300
may move to block 850. At block 850 the graph analyzer 324 may
order the queries in Q in descending order based on their R(q, q')
value. At block 860 the graph analyzer 324 may select the top N
queries with the highest R(q, q'), where N is any number, such as
five. Alternatively or in addition the service provider server 240
may identify the number of queries to be selected.
[0145] If the graph analyzer 324 determined that the graph was
pre-processed at block 820, the system 300 may move to 835. If the
graph was pre-processed, the steps of blocks 830, 840, 850 and 860
may have been performed offline. The N most relevant queries may
have been stored in a data structure, such as a hash map, keyed by
the query q. The offline processing may have generated a hash map
for every q in Q. Thus when a query q is received, the graph
analyzer 324 only needs to perform a quick lookup to identify the
queries most relevant to q. At block 835 the graph analyzer 324 may
perform a lookup to identify the queries most related to the query
q.
[0146] At block 870 the graph analyzer 324 may communicate the
original query q and the N most relevant queries to the ad serving
system 310. The ad serving system 310 may mark the relevant queries
as suggested queries from the query/advertisement network. Thus the
match type of the suggested queries may be the query/advertisement
network. The ad serving system 310 may serve advertisements which
bid on the suggested queries in addition to those which bid on the
query q of the user A 120A. The suggested queries may also be
communicated to the service provider 240. The service provider 240
may include the suggested queries on the search results page. The
suggested queries may assist the user A 120A in their search.
[0147] FIG. 9 is a flow chart illustrating the operations of using
a network of advertisers and users built by the system of FIG. 3,
or other systems for building a data structure representing a
network of advertisers and users, to determine the advertisements
most relevant to a query q. At block 810 the graph component 320
may receive a query q, such as from the service provider server
240. The query q may have been searched for by one of the users
120A-N, such as the user A 120A or the query q may be a set of
terms related to content on a page displayed to the user A 120A.
Links between the query q and advertisements may exist in the graph
data store 326. The query q may be communicated to the graph
analyzer 324.
[0148] At block 920 the graph analyzer 324 may determine whether
the graph data representing the query/advertisement graph was
pre-processed. The graph may be pre-processed offline to determine
all of the outputs that the graph may be utilized to generate, such
as the most relevant advertisements for a query q. The outputs for
a given query may be stored in a hashmap to enable quick and
efficient lookup of the data. In very large implementations of the
system 300 the processing time may require calculating outputs
offline. The steps that follow may be performed offline if the
graph data is pre-processed.
[0149] If the graph data was not pre-processed the system 300 may
move to block 930. At block 930 the graph analyzer 324 may identify
all queries Q and all advertisements A which are a part of a link
in the graph data store 326. At block 940 the graph analyzer 324
may calculate a relevance value R for each query q' in Q. The
relevance may indicate how relevant each query is to the query q
received from the service provider server 240. For a given query q'
in Q, the relevance value R of (q, q') may be determined by:
R ( q , q ' ) = a .di-elect cons. A ( w ( q , a , D q ) - W _ q ) (
w ( q ' , a , D q ' ) - W _ q ' ) a .di-elect cons. A q ( w ( q , a
, D q ) - W _ q ) 2 a .di-elect cons. A q ' ( w ( q ' , a , D q ' )
- W _ q ' ) 2 . ##EQU00009##
In R(q, q'), Dq may be the DUDE state of (q, a) and Dq' may be the
DUDE state of (q', a). The graph analyzer 324 may obtain the
weights, w(q,a,D), from the graph data store 326. W.sub.q may
represent the weight value for the position normalized click
through rate calculated by:
w ( q , a , D ) = COEC ( q , a , D ) = Clicks ( q , a , D ) I ( q ,
a , D ) refCTR ( D , r ) . ##EQU00010##
Alternatively or in addition W.sub.q may be calculated by:
W _ ( a ) = q .di-elect cons. Q a , D Clicks ( q , a ) COEC ( q , a
, D ) q .di-elect cons. Q a , D Clicks ( q , a ) . ##EQU00011##
[0150] Alternatively or in addition, in some situations, such as
when the distribution scales of the weights are relatively equal,
the relevance value R of (q, q') may be determined by:
R ( q , q ' ) = a .di-elect cons. A ( w ( q , a , D q ) ) ( w ( q '
, a , D q ' ) ) a .di-elect cons. A q ( w ( q , a , D q ) ) 2 a
.di-elect cons. A q ' ( w ( q ' , a , D q ' ) ) 2 .
##EQU00012##
[0151] After calculating R(q, q') for every q' in Q, the system 300
may move to block 950. At block 950 the graph analyzer 324 may
order the queries in Q in descending order based on their R(q, q')
value. At block 960 the graph analyzer 324 may select the top K
queries with the highest R(q, q'), where K may be any number, such
as five.
[0152] At block 970 the graph analyzer 324 may use the top K
queries, represented by q1, . . . , qK, to calculate a predicted
relevance between q and each advertisement a existing in A. The
graph analyzer may use the following formula to calculate the
predicted relevance w(q,a) for each a in A:
w ^ ( q , a ) = k = 1 K R ( q k , q ) w ( q k , a ) k = 1 K R ( q k
, q ) . ##EQU00013##
[0153] The values for R(q.sub.k, q) may have previously been
calculated at block 940. The values for w(q.sub.k,a) may be stored
in the graph data store 326.
[0154] Alternatively or in addition, the mean of the relevance
weight distribution may be decoupled from the estimation and the
mean may be added back into the final prediction as follows:
w ^ ( q * , a * ) = W _ q * + k = 1 K R ( q k , q * ) ( w ( q k , a
* ) - W _ q k ) k = 1 K R ( q k , q * ) . ##EQU00014##
[0155] W.sub.q may be the weight value for the position normalized
click through rate and may be determined by:
w ( q , a , D ) = COEC ( q , a , D ) = Clicks ( q , a , D ) I ( q ,
a , D ) refCTR ( D , r ) . ##EQU00015##
[0156] Once the predicted relevance w(q, a) has been calculated for
each a in A, the system 300 may move to block 980. At block 980 the
graph analyzer 324 may sort, in descending order, the predicted
relevances w(q,a) for each a in A. At block 985 the top D
advertisements most relevant to the query q may be selected, where
D may be any number, such as five.
[0157] If at block 920 the graph was pre-processed the system 300
may move to 935. If the graph was pre-processed, the steps of
blocks 930, 940, 950, 960, 970, and 980, and 985 may have been
performed offline. The most relevant advertisements may have been
stored in a data structure, such as a hash map, keyed by the query
q. The offline processing may have generated a hash map for every q
in Q. Thus when a query q is received, the graph analyzer 324 only
needs to perform a quick lookup to identify the advertisements most
relevant to q. At block 935 the graph analyzer 324 may perform a
lookup on the data structure to identify the top D advertisements
most relevant to the query q, where D may be any number, such as
five.
[0158] At block 990 the graph analyzer 324 may communicate the
original query q and the advertisements most relevant to q to the
service provider server 240 and/or the ad serving system 310. The
ad serving system 310 may use the most relevant advertisements to
supplement advertisements bid on for the query q. Alternatively or
in addition the graph analyzer 324 may communicate the most
relevant advertisements directly to the service provider server
240. In this case the service provider server 240 may only include
the advertisements identified by the graph analyzer 324 in the
search results of the user A 120A and bypass the ad serving system
310.
[0159] FIG. 10 is a flow chart illustrating the operations of using
a network of advertisers and users built by the system of FIG. 3,
or other systems for building a data structure representing a
network of advertisers and users, to determine the value attributed
to a suggested query. Determining the value attributed to a
suggested query may assist the revenue generators 110A-N in
optimizing their advertising campaigns by identifying the most
profitable queries. Alternatively or in addition to the value
attributed to a suggested query may indicate queries related to
content on a page. The revenue generators 110A-N may then focus
their advertising campaigns on the most profitable queries. The
service provider 130 may provide the revenue generators 110A-N with
reports indicating the effectiveness or value of each of the
queries they bid on. The reports may further indicate the value of
each query attributed to keyword suggestions and/or matching
systems. In some instances the revenue generators 110A-N may not
have been bidding on the suggested keywords and may modify their
advertising campaigns to include bids on the suggested
keywords.
[0160] At block 1005 the graph analyzer 324 may receive a query/ad
link (q, a) and a suggested query q' for the link (q, a). The
suggested query q' may have been previously suggested for q
resulting in the advertisement a being displayed to one of the
users 120A-N. Thus the link (q,a) may have a raw context stored in
the graph data store 326 that includes query suggestion q' and
match type M. The service provider server 240 may have communicated
(q, a) and q' to the graph analyzer 324.
[0161] At block 1010 the graph analyzer 324 may identify all
queries Q identified in the link data stored on the graph data
store 326. The graph analyzer 324 may calculate the relevance
values R(q, q'') for q and all q'' in Q, such as every query in Q.
After calculating the relevance values for all the queries, the
system 300 may move to block 1015. At block 1015 the graph analyzer
324 may sort the queries q'' in descending order based on their
relevance values R(q, q''). At block 1020 the graph analyzer 324
may identify the top K queries, where K is any number, such as
five. The graph analyzer 324 may place the top K queries into a
set, Q1(q). The steps described in blocks 1010, 1015 and 1020 may
be described in more detail in FIGS. 8 and 9 above.
[0162] At block 1025 the graph analyzer 324 may create a second
build of the graph without the link (q, a). If neither q nor a are
part of any other link, then the second build does not have to be
performed. At block 1030 the graph analyzer 324 may calculate the
relevancy values R(q, q'') based on the second build of the graph
for all q'' which may be an element of Q. At block 1035 the graph
analyzer 324 may sort the queries in descending order based on
their relevance values R(q, q''). At block 1040 the graph analyzer
324 may identify the top K queries from the second build, where K
is any number, such as five. The graph analyzer 324 may place the
top K queries from the second build into a second set, Q2(q). The
steps described in blocks 1030, 1035 and 1040 may be described in
more detail in FIGS. 8 and 9 above.
[0163] At block 1045 the graph analyzer 324 may determine a set of
queries HQ(q), calculated by Q1(q)-Q2(q). If neither q nor a are
part of any other link, and the second build was not performed,
then HQ(q)=Q1(q).
[0164] At block 1050 the graph analyzer 324 may calculate the
residual value of the link {umlaut over (.nu.)}(q,a), or the
summation of the value attributed to each query in HQ(q). The value
of each query in HQ(q) may be calculated by:
.nu.(q,a)=w(q,a)ppc(q,a). The weights of w(q,a) may be total clicks
or position normalized CTR. When the value of the weight is total
clicks, the measure of value may be simply a count of the total
aggregated revenue brought by the link over the time period T.
Alternatively or in addition the conversion rates may be used
instead of ppc. Thus the {umlaut over (.nu.)}(q,a) for the graph G
stored in the graph data store may be calculated by:
.upsilon. ( q , a , G ) = k .di-elect cons. HQ ( q , a ) .upsilon.
( k , a ) . ##EQU00016##
[0165] At block 1055 the graph analyzer 324 may add the value of
(q,a) to {umlaut over (.nu.)}(q,a,G) the total value provided by
each query in the set HQ(q). The calculation may be represented as:
.xi.(q',(q,a),M)=v((q, a))+{umlaut over (v)}((q, a), G), and the
result may be the value attributed to the suggestion q' for q with
match type M
[0166] At block 1060 the graph analyzer 324 may communicate the
value attributed to the suggested query q' to the service provider
server 240. The service provider server 240 may display the value
to one of the revenue generators 110A-N along with other data
describing the effectiveness of their advertising campaigns. The
revenue generators 110A-N may be able to improve their advertising
campaign by directly targeting suggested keywords with high
attributed values.
[0167] FIG. 11 is a flow chart illustrating the steps of using a
network of advertisers and users built by the system of FIG. 3, or
other systems for building a data structure representing a network
of advertisers and users, to determine the value attributed to a
match type and a suggested query. The operations illustrated in the
flowchart of FIG. 11 may require less computational complexity to
determine the value attributed to a suggested query than the
operations illustrated in the flowchart of FIG. 10.
[0168] The service provider 130 may be able to determine the values
attributed to each of the matching systems implemented in the ad
serving system 300. The service provider 130 may be able to
optimize ad serving by identifying the best performing matching
systems and the worst performing matching systems. The best
performing matching systems may be used more often and the poorly
performing matching systems may be slowly phased out. At block 110
the graph analyzer 324 may receive a query/ad link (q, a) and a
suggested query q' for the link (q, a). The suggested query q' may
have been previously suggested for q, resulting in the
advertisement a being displayed to one of the users 120A-N. Thus
the link (q, a) may have a raw context data stored in the graph
data store 326 that includes query suggestion q' and match type M.
The service provider server 240 may have communicated (q, a) and q'
to the graph analyzer 324.
[0169] At block 1120 the graph analyzer 324 may determine the match
type M of the suggested query q'. The match type may be obtained
from the raw context data of the query/ad link (q, a). At block
1130 the query analyzer may calculate the average residual value
for the link (q, a) attributed to the match type M The value
attributed to the match type M may be calculated by:
.zeta. ( M ) = 1 E ( ( q , a ) q ' ) .zeta. ( q ' , ( q , a ) , M )
, ##EQU00017##
where E is the total number of raw content for (q, a) with match
type M. The calculation of .xi.(q', (q,a), M) may be elaborated in
more detail in the operations illustrated in the flowchart of FIG.
10.
[0170] At block 1140 the graph analyzer 324 may calculate the value
attributed to the suggested query q' for the link (q, a). The value
may be calculated by: .xi.(q', (q, a))=v(q, a)+.xi.(M). Details on
the calculation of v(q, a) may be found in the operations of the
flowchart illustrated in FIG. 10. At block 1150 the graph analyzer
324 may communicate the average residual value attributed to the
match type M for the link (q,a) and the value attributed to the
keyword suggestion q' for q with match type M to the service
provider server 240.
[0171] FIG. 12 is a flow chart illustrating the steps of using a
network of advertisers and users built by the system of FIG. 3, or
other systems for building a data structure representing a network
of advertisers and users, to integrate valuable suggestions with
experimental or unknown suggestions. The service provider 130 may
benefit from identifying the best performing suggestions and
suggesting them at a higher rate than lower performing suggestions.
Furthermore there may be valuable queries that are not suggested
because they may be new, obscure, or otherwise have not been
exposed to a large amount of traffic. The service provider 130 may
benefit from experimenting with these terms to determine if any of
them may be profitable.
[0172] The query suggestions stored in the graph data store 326 may
be separated into two sets, one where the value is known and
another where the value is unknown. A suggestion may have a known
value if the value is non-zero and the suggestion has been exposed
to a minimal amount of traffic, i.e., its impression count exceeds
some minimum threshold. At block 1210 the graph analyzer 324 may
calculate the value provided by each suggestion in the graph
historical dataset. The value of the suggestions may be calculated
via the methods outline in FIGS. 10 and 11, namely through the
equation: .xi.(q', (q, a))=v(q, a)+.xi.(M). Alternatively or in
addition the value may be defined by relevance, or weight, such as
total clicks or conversions.
[0173] At block 1220 the graph analyzer 324 may sort the
suggestions based on their attributed values calculated above. At
block 1230, the graph analyzer 324 may place the suggestions with
the top K known values into an exploit set, where K is any number,
such as fifty. At block 1240 the graph analyzer 324 may place
unknown values, such as the next J values, into an explore set. The
explore set may include suggestions with unknown values or
suggestions with known values without enough traffic exposure. The
explore set may allow the system 300 to experiment with past and
new suggestions from any match type. The cardinality of the explore
set may be orders of magnitude larger than that of the exploit set.
Suggestions in the explore set may be scored for relevance or
value, such as by any of the aforementioned methods of valuing
suggestions.
[0174] The explore set may be separated into two subsets, a live
set and an offline set. The live set may be the explore suggestions
that are in trial, or under experimentation. The offline set may be
suggestions that are not currently being used.
[0175] At block 1250 the graph analyzer 324 may communicate the
sets of queries to the service provider server 240, and/or the ad
serving system 310. The service provider server 240 and/or the ad
serving system may suggest the suggestions from the exploit set and
the explore-live set. The exploit set may be suggested more
frequently than the explore-live set to benefit from the proven
value provided by the exploit set.
[0176] At block 1260, the graph analyzer 324 may repeat the
operations after an interval of time T has passed. The operations
may be continually repeated at the interval of time T. The
suggestions may be re-valued and re-sorted. Top valued suggestions
with sufficient exposure may comprise the exploit set. The live
explore set may be populated with a new batch of suggestions with
unknown values. The continual update of the exploit set and the
explore-live set may ensure that the exploit set captures any
seasonal queries or other queries whose value may increase due to
temporal externalities.
[0177] FIG. 13 illustrates an exemplary page 1300 displaying
advertisements. The page 1300 may be served by the service provider
130 to the users 120A-N and may be a web page displayed on the
Internet. The page 1300 may include content 1310, such as a list of
search results, which may generally be the purpose of the page. The
page 1300 may be shown with slots for four advertisements. There
may be two top ad slots 1320, 1330 and two side ad slots 1340,
1350. The number of ads in the top ad slots 1320, 1330 may
determine the DUDE state of the query. The service provider 130 may
attempt to fill the ad slots 1320, 1330, 1340, 1350 with
advertisements from the sponsored search server 312, or from the
graph analyzer 324.
[0178] FIG. 14 is a screenshot of a page 1400 displaying
advertisements to the users 120A-N served from a search engine
marketing system implementing a system for building a data
structure representing a network of advertisers and users. The page
1400 may be displayed to one of the users 120A-N, such as the user
A 120A, when the user A 120A searches for the term "plasma." The
page 1400 may include a search query 1405, content 1410, query
suggestions 1460, top ads 1420, side ads 1430 and a popup ad 1470.
The content 1410 may include a search results list 1440 based on
the search query 1405 submitted by the user A 120A, such as
"plasma". The search results list 1440 may include one or more
search results 1450. A search result 1450 may include a title link
1452, a URL 1454, a description 1456 and a rank 1458. The top ads
1420 may include one or more sponsor listings 1422. The side ads
1430 may include sponsored listings. The query suggestions 1460 may
represent queries that were suggested by the query analyzer 324.
The queries may represent phrases similar to the search query 1405
that users 120A-N searched for. The query suggestions 1460 may have
been generated by the system of FIG. 3.
[0179] The title link 1452 may be a clickable link that may
reference a site. If one the users 120A-N, such as the user A 120A,
clicks on the title link 1452, the user A 120A may be forwarded to
the site referred to by the title link 1452. The site referred to
by the title link 1452 may be described in the description 1456.
The URL 1454 may represent the URL of the site referred to by the
link 1452. The rank 1458 may represent the order of the search
result 750 in the search results list 1440.
[0180] The top ads 1420 and the side ads 3140 may include any
combination of sponsored listings, banner ads and popup ads. The
top ads 1420 and the side ads 1430s may represent advertisements
that may have been retrieved from the sponsored search server 312,
the content match server 316 or the graph analyzer 324. The number
of ads in the top ads 1420 may indicate the DUDE state of the
query. The sponsored listing 1422 and/or the banner ad 1424 may
link the users 120A-N to the web site of a revenue generator, such
as the revenue generator A 110A, when the users 120A-N click on the
banner ad 1424 and/or the sponsored listing 1422. The banner ad
1424 may be constructed from an image (GIF, JPEG, PNG), a
JavaScript program or a multimedia object employing technologies
such as Java, Shockwave or Flash. The banner ad 1424 may employ
animation, video, or sound to maximize presence. The images used in
the banner ad 1424 may be in a high-aspect ratio shape (i.e. either
wide and short, or tall and narrow).
[0181] The popup ad 1470 may link the users 120A-N to the web site
of a revenue generator, such as the revenue generator A 110A, when
the users 120A-N click on the popup ad 1470. The popup ad 1470 may
be constructed from an image (GIF, JPEG, PNG), a JavaScript program
or a multimedia object employing technologies such as Java,
Shockwave or Flash. The popup ad 1470 may employ animation, video,
or sound to maximize presence. The popup ad 1470 may run in the
same window as the page, or may open in a new window. The popup ad
1470 may be capable of being closed and/or minimized by clicking on
an `X` in the corner of the popup ad 1470.
[0182] FIG. 15 illustrates a general computer system 1500, which
may represent a service provider server 240, a third party server
250, an advertising services server 260, a graph component 320, a
graph processor 322, a graph analyzer 324 or any of the other
computing devices referenced herein. The computer system 1500 may
include a set of instructions 1524 that may be executed to cause
the computer system 1500 to perform any one or more of the methods
or computer based functions disclosed herein. The computer system
1500 may operate as a standalone device or may be connected, e.g.,
using a network, to other computer systems or peripheral
devices.
[0183] In a networked deployment, the computer system may operate
in the capacity of a server or as a client user computer in a
server-client user network environment, or as a peer computer
system in a peer-to-peer (or distributed) network environment. The
computer system 1500 may also be implemented as or incorporated
into various devices, such as a personal computer (PC), a tablet
PC, a set-top box (STB), a personal digital assistant (PDA), a
mobile device, a palmtop computer, a laptop computer, a desktop
computer, a communications device, a wireless telephone, a
land-line telephone, a control system, a camera, a scanner, a
facsimile machine, a printer, a pager, a personal trusted device, a
web appliance, a network router, switch or bridge, or any other
machine capable of executing a set of instructions 1524 (sequential
or otherwise) that specify actions to be taken by that machine. In
a particular embodiment, the computer system 1500 may be
implemented using electronic devices that provide voice, video or
data communication. Further, while a single computer system 1500
may be illustrated, the term "system" shall also be taken to
include any collection of systems or sub-systems that individually
or jointly execute a set, or multiple sets, of instructions to
perform one or more computer functions.
[0184] As illustrated in FIG. 15, the computer system 1500 may
include a processor 1502, such as, a central processing unit (CPU),
a graphics processing unit (GPU), or both. The processor 1502 may
be a component in a variety of systems. For example, the processor
1502 may be part of a standard personal computer or a workstation.
The processor 1502 may be one or more general processors, digital
signal processors, application specific integrated circuits, field
programmable gate arrays, servers, networks, digital circuits,
analog circuits, combinations thereof, or other now known or later
developed devices for analyzing and processing data. The processor
1502 may implement a software program, such as code generated
manually (i.e., programmed).
[0185] The computer system 1500 may include a memory 1504 that can
communicate via a bus 1508. The memory 1504 may be a main memory, a
static memory, or a dynamic memory. The memory 1504 may include,
but may not be limited to computer readable storage media such as
various types of volatile and non-volatile storage media, including
but not limited to random access memory, read-only memory,
programmable read-only memory, electrically programmable read-only
memory, electrically erasable read-only memory, flash memory,
magnetic tape or disk, optical media and the like. In one case, the
memory 1504 may include a cache or random access memory for the
processor 1502. Alternatively or in addition, the memory 1504 may
be separate from the processor 1502, such as a cache memory of a
processor, the system memory, or other memory. The memory 1504 may
be an external storage device or database for storing data.
Examples may include a hard drive, compact disc ("CD"), digital
video disc ("DVD"), memory card, memory stick, floppy disc,
universal serial bus ("USB") memory device, or any other device
operative to store data. The memory 1504 may be operable to store
instructions 1524 executable by the processor 1502. The functions,
acts or tasks illustrated in the figures or described herein may be
performed by the programmed processor 1502 executing the
instructions 1524 stored in the memory 1504. The functions, acts or
tasks may be independent of the particular type of instructions
set, storage media, processor or processing strategy and may be
performed by software, hardware, integrated circuits, firm-ware,
micro-code and the like, operating alone or in combination.
Likewise, processing strategies may include multiprocessing,
multitasking, parallel processing and the like.
[0186] The computer system 1500 may further include a display 1514,
such as a liquid crystal display (LCD), an organic light emitting
diode (OLED), a flat panel display, a solid state display, a
cathode ray tube (CRT), a projector, a printer or other now known
or later developed display device for outputting determined
information. The display 1514 may act as an interface for the user
to see the functioning of the processor 1502, or specifically as an
interface with the software stored in the memory 1504 or in the
drive unit 806.
[0187] Additionally, the computer system 1500 may include an input
device 812 configured to allow a user to interact with any of the
components of system 1500. The input device 812 may be a number
pad, a keyboard, or a cursor control device, such as a mouse, or a
joystick, touch screen display, remote control or any other device
operative to interact with the system 1500.
[0188] The computer system 1500 may also include a disk or optical
drive unit 806. The disk drive unit 806 may include a
computer-readable medium 1522 in which one or more sets of
instructions 1524, e.g. software, can be embedded. Further, the
instructions 1524 may perform one or more of the methods or logic
as described herein. The instructions 1524 may reside completely,
or at least partially, within the memory 1504 and/or within the
processor 1502 during execution by the computer system 1500. The
memory 1504 and the processor 1502 also may include
computer-readable media as discussed above.
[0189] The present disclosure contemplates a computer-readable
medium 1522 that includes instructions 1524 or receives and
executes instructions 1524 responsive to a propagated signal; so
that a device connected to a network 235 may communicate voice,
video, audio, images or any other data over the network 235.
Further, the instructions 1524 may be transmitted or received over
the network 235 via a communication interface 1518. The
communication interface 1518 may be a part of the processor 1502 or
may be a separate component. The communication interface 1518 may
be created in software or may be a physical connection in hardware.
The communication interface 1518 may be configured to connect with
a network 235, external media, the display 1514, or any other
components in system 1500, or combinations thereof. The connection
with the network 235 may be a physical connection, such as a wired
Ethernet connection or may be established wirelessly as discussed
below. Likewise, the additional connections with other components
of the system 1500 may be physical connections or may be
established wirelessly. In the case of a service provider server
240, a third party server 250, an advertising services server 260,
the servers may communicate with users 120A-N and the revenue
generators 110A-N through the communication interface 1518.
[0190] The network 235 may include wired networks, wireless
networks, or combinations thereof. The wireless network may be a
cellular telephone network, an 802.11, 802.16, 802.20, or WiMax
network. Further, the network 235 may be a public network, such as
the Internet, a private network, such as an intranet, or
combinations thereof, and may utilize a variety of networking
protocols now available or later developed including, but not
limited to TCP/IP based networking protocols.
[0191] The computer-readable medium 1522 may be a single medium, or
the computer-readable medium 1522 may be a single medium or
multiple media, such as a centralized or distributed database,
and/or associated caches and servers that store one or more sets of
instructions. The term "computer-readable medium" may also include
any medium that may be capable of storing, encoding or carrying a
set of instructions for execution by a processor or that may cause
a computer system to perform any one or more of the methods or
operations disclosed herein.
[0192] The computer-readable medium 1522 may include a solid-state
memory such as a memory card or other package that houses one or
more non-volatile read-only memories. The computer-readable medium
1522 also may be a random access memory or other volatile
re-writable memory. Additionally, the computer-readable medium 1522
may include a magneto-optical or optical medium, such as a disk or
tapes or other storage device to capture carrier wave signals such
as a signal communicated over a transmission medium. A digital file
attachment to an e-mail or other self-contained information archive
or set of archives may be considered a distribution medium that may
be a tangible storage medium. Accordingly, the disclosure may be
considered to include any one or more of a computer-readable medium
or a distribution medium and other equivalents and successor media,
in which data or instructions may be stored.
[0193] Alternatively or in addition, dedicated hardware
implementations, such as application specific integrated circuits,
programmable logic arrays and other hardware devices, may be
constructed to implement one or more of the methods described
herein. Applications that may include the apparatus and systems of
various embodiments may broadly include a variety of electronic and
computer systems. One or more embodiments described herein may
implement functions using two or more specific interconnected
hardware modules or devices with related control and data signals
that may be communicated between and through the modules, or as
portions of an application-specific integrated circuit.
Accordingly, the present system may encompass software, firmware,
and hardware implementations.
[0194] The methods described herein may be implemented by software
programs executable by a computer system. Further, implementations
may include distributed processing, component/object distributed
processing, and parallel processing. Alternatively or in addition,
virtual computer system processing maybe constructed to implement
one or more of the methods or functionality as described
herein.
[0195] Although components and functions are described that may be
implemented in particular embodiments with reference to particular
standards and protocols, the components and functions are not
limited to such standards and protocols. For example, standards for
Internet and other packet switched network transmission (e.g.,
TCP/IP, UDP/IP, HTML, HTTP) represent examples of the state of the
art. Such standards are periodically superseded by faster or more
efficient equivalents having essentially the same functions.
Accordingly, replacement standards and protocols having the same or
similar functions as those disclosed herein are considered
equivalents thereof.
[0196] The illustrations described herein are intended to provide a
general understanding of the structure of various embodiments. The
illustrations are not intended to serve as a complete description
of all of the elements and features of apparatus, processors, and
systems that utilize the structures or methods described herein.
Many other embodiments may be apparent to those of skill in the art
upon reviewing the disclosure. Other embodiments may be utilized
and derived from the disclosure, such that structural and logical
substitutions and changes may be made without departing from the
scope of the disclosure. Additionally, the illustrations are merely
representational and may not be drawn to scale. Certain proportions
within the illustrations may be exaggerated, while other
proportions may be minimized. Accordingly, the disclosure and the
figures are to be regarded as illustrative rather than
restrictive.
[0197] Although specific embodiments have been illustrated and
described herein, it should be appreciated that any subsequent
arrangement designed to achieve the same or similar purpose may be
substituted for the specific embodiments shown. This disclosure is
intended to cover any and all subsequent adaptations or variations
of various embodiments. Combinations of the above embodiments, and
other embodiments not specifically described herein, may be
apparent to those of skill in the art upon reviewing the
description.
[0198] The Abstract is provided with the understanding that it will
not be used to interpret or limit the scope or meaning of the
claims. In addition, in the foregoing Detailed Description, various
features may be grouped together or described in a single
embodiment for the purpose of streamlining the disclosure. This
disclosure is not to be interpreted as reflecting an intention that
the claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter may be directed to less than all of the
features of any of the disclosed embodiments. Thus, the following
claims are incorporated into the Detailed Description, with each
claim standing on its own as defining separately claimed subject
matter.
[0199] The above disclosed subject matter is to be considered
illustrative, and not restrictive, and the appended claims are
intended to cover all such modifications, enhancements, and other
embodiments, which fall within the true spirit and scope of the
description. Thus, to the maximum extent allowed by law, the scope
is to be determined by the broadest permissible interpretation of
the following claims and their equivalents, and shall not be
restricted or limited by the foregoing detailed description.
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