U.S. patent application number 13/312665 was filed with the patent office on 2012-12-06 for method and apparatus for context-based content recommendation.
This patent application is currently assigned to Baynote, Inc.. Invention is credited to Scott Brave, Jack Jia.
Application Number | 20120311139 13/312665 |
Document ID | / |
Family ID | 40341772 |
Filed Date | 2012-12-06 |
United States Patent
Application |
20120311139 |
Kind Code |
A1 |
Brave; Scott ; et
al. |
December 6, 2012 |
Method and Apparatus for Context-Based Content Recommendation
Abstract
Starting with the people in and around enterprises, the
expertise and work patterns stored in people's brains as exhibited
in their daily behavior is detected and captured. A behavioral
based knowledge index is thus created that is used to produce
expert-guided, personalized information.
Inventors: |
Brave; Scott; (San Jose,
CA) ; Jia; Jack; (Los Altos Hills, CA) |
Assignee: |
Baynote, Inc.
San Jose
CA
|
Family ID: |
40341772 |
Appl. No.: |
13/312665 |
Filed: |
December 6, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12188850 |
Aug 8, 2008 |
8095523 |
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13312665 |
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11319928 |
Dec 27, 2005 |
7698270 |
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12188850 |
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60640872 |
Dec 29, 2004 |
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60954677 |
Aug 8, 2007 |
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Current U.S.
Class: |
709/224 |
Current CPC
Class: |
G06F 16/9535
20190101 |
Class at
Publication: |
709/224 |
International
Class: |
G06F 15/173 20060101
G06F015/173 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 8, 2008 |
US |
PCT/US2008/072660 |
Claims
1. A computer implemented context-centric content recommendation
method, comprising: establishing a current context for a user at a
Web site based upon inputs that are representative of user actions
at the Web site; capturing the user's current context information
with an observer tag that is embedded in the Web site; determining
the user's interest based on various user behaviors collected by
the observer tag; analyze the user behaviors; storing all resulting
information in a memory as the user's current context vector
including a hybrid vector of terms and documents with weights on
each entry reflecting how strongly that term or document reflects
the user's current context; incrementing the context vector entries
corresponding to terms and phrases selected as a user captures
expressed interest; decrementing corresponding entries as user
actions move further into the past; incrementing a corresponding
vector entry for documents that a user selects, as determined based
on a user's implicit actions; and generating a representation of
the user's current context as a context vector for use in making
recommendations of content to said user.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 12/188,85, filed Aug. 8, 2008 which claims priority to U.S.
provisional patent application Ser. No. 60/954,677, filed Aug. 8,
2007 and is a continuation-in-part of U.S. patent application Ser.
No. 11/319,928, filed Dec. 27, 2005, which application claims
priority to U.S. provisional patent application Ser. No.
60/640,872, filed Dec. 29, 2004, all of which are incorporated
herein in their entirety by this reference thereto.
BACKGROUND OF THE INVENTION
[0002] 1. Technical Field
[0003] The invention relates to electronic access to information.
More particularly, the invention relates to a method and apparatus
for context-based content recommendation.
[0004] 2. Description of the Prior Art
[0005] One problem with finding information in an electronic
network concerns how people are connected as quickly and
effectively as possible with the information/products/services that
meet their needs. This has been one of the main goals of Web pages
and search engines since the beginning of the World Wide Web.
Failure to do so leads to lost business in the case of eCommerce
eTravel, and eMarketing sites, frustrated customers on eSupport
sites who likely then call customer support, thus wasting a lot of
the company's money, disinterested viewers/reader on eMedia sites
who quickly abandon the site, thus losing opportunities for
advertising revenue, and unproductive employees on intranets.
[0006] Web site design is a manual attempt to solve the problem of
information discovery: to organize information in a way that the
designer imagines helps a user find what they are looking for.
While effective in some cases, trying to find information in this
way often is slow and ineffective as users resort to poking around
a site looking for the information they need. Most users actually
abandon a site if they do not find what they are looking for within
three clicks. One problem is that the site is static. In more
recent years, Web analytics has emerged as an attempt to alleviate
this problem. Designers can see all of the actions that happen on
their site and collect it into reports that aim to provide some
guidance on how the site can be redesigned or reconfigured more
effectively. While providing some benefit, the information provided
is often ambiguous and provides only hints rather than concrete
suggestions for improvement. At best, the process is tedious,
requires a great deal of manual effort as designers redesign the
site in line with learnings, and takes a long time. The feedback
loop is thus slow and ineffective.
[0007] Automatic content recommendation is a completely different
strategy that emerged very early in the life of the Web. Search
engines, such as Google, Yahoo, and Ask, are the common
manifestation of such techniques. The basic idea is that the user
explicitly describes what they are looking for in the form of a
search query, and an automatic process attempts to identify the
piece of content, most often a Web page, that best matches their
query. The approach for doing this amounts to looking at all
possible documents and recommending those where the target query
occurs within the text with highest frequency, i.e. keyword match.
Modern adaptations of this basic technique add layers of
sophistication, e.g. natural language processing, but the key in
these approaches is still to use properties of the content itself,
e.g. words within the document, to determine the ultimate relevancy
ranking This represents the first content-centric phase of content
recommendation (see FIG. 1).
[0008] Many variations to this approach exist including, most
notably, meta-tagging. In this approach, the content creator
selects a small number of terms to describe the content. These
terms are embedded within the content, often as HTML meta-tags, but
are not necessarily made visible to the consumer of the content.
This is one way to allow search engines to search content that is
not text-based, such as video clips. This approach was very common
in the late 1990's, but has since fallen out of favor due to the
enormous effort required to keep the meta-tags up to date and
in-synch with changes to the content.
[0009] In many ways, this first content-centric approach on the
surface make a lot of sense, i.e. if you want to recommend content,
consider the content itself. A key problem with this approach is
that it often brings back lots of documents that may be relevant
but not useful. Many documents may exhibit a strong keyword match,
but are outdated or not truly relevant to the user's current
interest. If users do not find a useful result within the first few
results, they are most likely going to abandon the search.
[0010] Keyword match does not really reflect how we find
information most efficiently in the real world. In day-to-day life,
the best way to find the information/products/services we are
looking for is to ask someone who knows to point us in the right
direct. The second phase of content recommendation thus shifts the
focus from content to users (see FIG. 1). Google's "PageRank"
algorithm, though we place it in phase 1, was really a transitional
technology that harkened the coming of phase 2. The page rank
algorithm's break-through was to consider not only the content of
the page itself, but how it had been linked to from other pages by
other Web site designers. This represented a form of voting on the
importance of Web pages. Thus, pages that were linked to more often
were seen as more valuable. While bringing people into the
equation, the people who were voting were Web designers rather than
the consumers of the content, i.e. the users. Phase 2 of content
recommendation is all about the users. The three most well known
approaches that fall into phase 2 are: folksonomy,
profiling/behavioral targeting, and collaborative filtering.
Folksonomy
[0011] The first, folksonomy, represents the most straight-forward
addition to phase 1. Here, users are allowed to tag content
themselves. So, rather than the Web site designers, or a single
designer, being responsible for coming up with the best set of
keywords to describe the content, folksonomy lets the community do
it. Once this is done, those community created tags essentially
become part of the content and can be searched using traditional
information retrieval/search techniques developed in phase 1. A big
assumption in this approach is that the subset of the community who
takes the time to tag the pages explicitly, ultimately produce a
description that is valid and representative of the larger
community's opinion. This is often not the case.
Profiling/Behavioral Targeting
[0012] Profiling/Behavioral targeting in its common form also
borrows heavily from phase 1 techniques. Here, based on a user's
prior behavior on a site, e.g. the pages clicked or products
purchased, a profile is built for that user. This profile may, in
the simple case, be based on a collection of pages clicked or
products purchased. The profile may also make use of the content
itself or meta-tags to attempt to discern the user's historical
topics of interest. For example, if a user purchased many films
tagged as "horror" by content providers in the past, then a
behavioral targeting system would tend to recommend more "horror"
films to the user. A major assumption here is that a user's
historical behaviors are a good predictor of future interest. While
sometimes true, this assumption tends to fail at least as often as
it works. The reason for failure is that people exhibit a variety
of behaviors depending on their current interests, context, and
goals. For example, someone who bought a few books on guitar as a
one-time gift for his wife a few weeks ago, might continue to be
recommended guitar books by a behavioral targeting approach, even
though he may no longer have interest in that topic. Profiling
approaches often also take into account demographic data of users,
such as age, gender, and geographic location. The core belief
underlying such approaches is: If I only knew enough about a user I
could predict exactly what they want. However, some basic
introspection uncovers the fallacy underlying this approach. For
example, I may know more about my wife than any person or machine.
I am in this way the ideal profiling system for her. However, I am
unable to predict what she might be currently looking for online
without some context.
Collaborative Filtering
[0013] Collaborative filtering is another user-centric approach
which is arguably the most strictly user-centric. Here, users are
compared to one another based on common purchases, click histories,
or explicit ratings. For example, based on a person's previous
ratings of movies on a movie site, find other people who most agree
with that person's ratings and recommend other movies that he
liked. Standard "people who bought this also bought that"
approaches are actually a variation on the collaborative filtering
approach, where a user's most recent action serves as the sole
basis for identifying similar users. This approach was made popular
by Amazon's recommendation engine. A big assumption in this
approach is that some global similarity measure between users based
on past behavior is a useful way to predict future interest. This
is a flawed assumption, however. One may be very similar to some of
his co-workers in a work context, e.g. they are all Java engineers,
with similar interests regarding programming, but quite different
from these co-workers when outside of the office, on the golf
course for instance. In the context of golf, one likely has a very
different peer group. Grouping users at a global level is more
often misleading than helpful.
[0014] Another weakness in all of the user-centric approaches in
phase 2 is the reliance on either explicit measures of liking or
overly-simplistic implicit measures. Explicit measures include
asking the user to indicate their liking of a particular piece of
content, e.g. on a 1-5 scale. Such approaches are almost always
biased because they represent a very small percentage of the
population. Further, the people who are taking the time to do these
ratings are not representative of the community as a whole. They
tend to be very opinionated or reflect a specific personality type
that is willing to spend the time to voice their opinion.
[0015] Those approaches that leverage implicit observations as a
rule either look at clicks or purchases. Clicks are a flawed way to
assess liking because getting someone to click on a result has a
lot more to do with an intriguing, perhaps even ambiguous, title
and location on page. It tells one nothing about how a user felt
about the content once it is selected for viewing. At the other
extreme, many systems use purchases as a measure of liking. While
purchases are a reasonable way to assess this, they are too
limited. For example, when buying a camera, one may seriously
consider a number of products before making a decision. All of that
information could be valuable to others interested in cameras above
and beyond to the one ultimately purchased.
SUMMARY OF THE INVENTION
[0016] An embodiment of the invention represents Phase 3 in the
evolution of content recommendation (see FIG. 1). Here, the idea is
to start by understanding the current user's context, i.e.: What is
their intent? What are they looking for? Based on this
understanding, then find the appropriate peer group representing
other users who are most like the current user in the context of
this identified interest. From there, find the content that that
peer group identifies as most relevant to the current context.
[0017] The approach taken in the invention is context-centric or,
put another way, intent-centric. The techniques used to achieve
this approach are described later and are fundamentally based on
the UseRank technology and affinity engine described, in part, in
U.S. patent application Ser. No. 11/319,928, filed Dec. 27, 2005,
which is incorporated herein in its entirety by this reference
thereto. It should be noted that all previous approaches, including
the content-centric and user-centric approaches, are subsumed and
improved by the Phase 3 approach manifested in the invention.
Because the invention adds the dimension of context to the picture
on top of users and content, it is always possible to choose to
ignore context and use the system to provide phase 2 functionality,
such as collaborative filtering or behavioral targeting/profiling.
However, even these previously known approaches are significantly
improved in their functionality based on a critical aspect of the
invention which provides full-spectrum behavioral fingerprints.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1 is a schematic overview of the evolution of content
recommendation leading up to the invention;
[0019] FIG. 2 is an architectural schematic diagram showing a
method and apparatus for context-based content recommendation
according to the invention;
[0020] FIG. 3 is a schematic architectural diagram showing affinity
engine integration according to the invention;
[0021] FIG. 4 is a schematic architectural diagram showing a
dynamic, adaptive, real-time platform of community wisdom and
community guided Web according to the invention;
[0022] FIG. 5 is an architectural schematic diagram showing a
community wisdom platform according to the invention;
[0023] FIG. 6 is schematic flow diagram showing the process of
translating context to recommendations;
[0024] FIG. 7 is schematic flow diagram showing the process for
achieving a virtual folksonomy according to the invention;
[0025] FIG. 8 is a schematic diagram showing topic attraction,
topic match, and activeness according to the invention;
[0026] FIG. 9 is a schematic flow diagram showing the behavioral
fingerprinting process according to the invention;
[0027] FIG. 10 is a graph showing a long tail marketing model with
an architectural overlay according to the invention;
[0028] FIG. 11 is a "Wisdom of Crowds" pseudo-equation;
[0029] FIG. 12 is a schematic flow diagram showing an affinity
engine memory prediction machine according to the invention;
[0030] FIG. 13 is a screen shot of the welcome screen of the
customer portal according to the invention;
[0031] FIG. 14 is a screenshot of a lift report within the customer
portal according to the invention;
[0032] FIG. 15 is a screenshot showing community-guided e-commerce
according to the invention, including recommendations for competing
and complementary products according to the invention;
[0033] FIG. 16 is a screenshot showing community-guided e-travel
according to the invention;
[0034] FIG. 17 is a screenshot showing community-guided marketing
according to the invention;
[0035] FIG. 18 is a screenshot showing social search and community
information popup/overlay according to the invention;
[0036] FIG. 19 is a screenshot showing community-guided online
support according to the invention;
[0037] FIG. 20 is a screenshot showing a community
intranet/knowledge portal according to the invention;
[0038] FIG. 21 is a screenshot showing community-guided media
according to the invention;
[0039] FIG. 22 is a screenshot showing community-guided social
search for media according to the invention;
[0040] FIG. 23 is a screenshot and schematic diagram showing
community-guided cross-site recommendations for media according to
the invention;
[0041] FIG. 24 is a screenshot showing community-guided video
recommendations according to the invention;
[0042] FIG. 25 is a screenshot showing community-guided topic
recommendations according to the invention;
[0043] FIG. 26 is a schematic flow diagram showing the
community-guided context-relevant Ad recommendations according to
the invention;
[0044] FIG. 27 is a schematic flow diagram showing a live
connection according to the invention;
[0045] FIG. 28 is a block schematic diagram showing the system
architecture of a preferred embodiment of the invention;
[0046] FIG. 29 is a block schematic diagram showing code snippets
of the client integration through JavaScript tags and REST in a
preferred embodiment of the invention; and
[0047] FIG. 30 is a block schematic diagram showing an AJAX
platform according to the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0048] The invention represents Phase 3 in the evolution of content
recommendation (see FIG. 1). Here, the idea is to start by
understanding the current user's context, i.e.: What is their
intent? What are they looking for? Based on this understanding,
then find the appropriate peer group representing other users who
are most like the current user in the context of this identified
interest. From there, find the content that that peer group
identifies as most relevant to the current context.
[0049] The approach taken in the invention is context-centric or,
put another way, intent-centric. The techniques used to achieve
this approach are described later and are fundamentally based on
the UseRank technology and affinity engine described, in part, in
U.S. patent application Ser. No. 11/319,928, filed Dec. 27, 2005.
It should be noted that all previous approaches, including the
content-centric and user-centric approaches, are subsumed and
improved by the Phase 3 approach manifested in the invention.
Because the invention adds the dimension of context to the picture,
on top of users and content, it is always possible to choose to
ignore context and use the system to provide collaborative filter,
profiling. However, even these previously known approaches are
significantly improved in their functionality based on a critical
aspect of the invention which provides full-spectrum behavioral
fingerprints.
[0050] FIG. 2 is an architectural schematic diagram showing a
method and apparatus for context-based content recommendation
according to the invention.
[0051] Full-spectrum behavioral fingerprints provide a significant
advancement over current state-of-the-art implicit ratings, which
essentially amount to click analysis and purchase behavior. First,
they take into account a wide variety of user behaviors including,
but not limited to, clicks, time spent on a page, scrolling and
mouse movement, explicit actions such as print, email, bookmark,
links used, frequency of return visits, and searches performed.
Second, all of these behaviors can be cross-correlated with the
current user's behaviors on other pages, and also with the rest of
the community's and identified peers' behaviors on the current
page. From this analysis, a probability that the user has found
value in a particular piece of content can be discerned and fed
into the learning system.
[0052] A further aspect of the invention concerns the seamless
integration of existing strategies for automatic content
recommendation (see FIG. 3), including search engines 301 and
profiling systems 302, and ad servers 303, as well as systems for
manual recommendations including merchandising rules and systems
304. The invention also seamlessly integrates with other
information sources related to the content, such as product
catalogs 305, which can be used for purposes of display, filtering,
or learning.
[0053] Finally, because of the comprehensive nature of the
information collected and the learned affinities, the system
represents a general wisdom platform 501 on top of which many
applications can be built (see FIG. 4 and FIG. 5), such as
SocialSearch 502, Content 503 and Product 504 Recommendations,
Insights 505, eMail 506, and Live connect 507 which are tailored to
various applications, e.g. eCommerce, eMarketing, etc. Additional
applications include reports and integrations with bid management
systems for search engine optimization and search engine marketing
508, mobile and IPTV 509, and custom applications or mashups 510.
Some of these applications are described later. Each of these
critical aspects are now described in greater detail
Context- and Intent-Centric
[0054] A core advancement in the evolution of content
recommendation embodied by invention is the process for identifying
and representing the users current topic of interest and converting
that interest into a set of useful recommendations and information.
The steps in this process are shown in FIG. 6 and outlined
below:
[0055] Step 1: When a user comes to a Web site, they immediately
begin to establish their current context. They might do this by
entering a query into a search box on the site, navigating to a
particular section, or may even have established some context
before arriving at the site by doing a search on an external search
engine, such as Goggle or Yahoo, that led to this site. All of this
information is captured by an Observer Tag, i.e. a piece of
HTML/JavaScript embedded in the Web site. As the user continues to
move through the site, they may also show interest in a particular
page or piece of content, based on their implicit actions. Interest
is determined based on various behaviors collected by the Observer
Tag and analyzed using the invention's Full Spectrum Behavioral
Fingerprint technology 601 (described in greater detail below).
These pages of interest further contribute to the user's
context.
[0056] All of this information is stored as the user's current
context vector 602, which is a hybrid vector of terms and documents
with weights on each entry reflecting how strongly that term or
document reflects the user's current context. As a user enters
search terms and/or clicks navigation links, the vector entries
corresponding to the terms and phrases entered or clicked are
incremented to capture expressed interest. As these actions move
further into the past, the corresponding entries are decremented or
decayed. Similarly, documents that a user clicks on or indicates
interest in, as determined based on their implicit actions,
increment the corresponding vector entry to a degree based on the
level of interest and level of certainty determined by the
invention. The result is a representation of the user's current
context as a context vector. It is also possible to increment the
context vector further, based on historical actions and historical
contexts of interest of the user. Although this is not generally
done in the preferred embodiment because such information can be
misleading, it is possible to increment the context vector to a
lesser degree based on these historical contexts in applications
where historical behavior is considered to be relevant.
[0057] Step 2: Expand and refine the context vector into an intent
vector 603, based on affinities/associations learned from the
aggregated wisdom 604 collected from observations on the community
as a whole over a longer term. For example, the user may have
entered a query about digital SLR cameras and expressed implicit
interest in a Nikon page. In the context vector, the entries
corresponding to "digital SLR camera" as well as "digital," "SLR,"
and "camera" to a lesser degree are incremented, as is vector entry
corresponding to the particular Nikon page of interest. To create
the intent vector, the system looks at affinities between the terms
and documents in the context vector to other terms. For example,
the community wisdom may have discovered that the term
"high-resolution" may be highly associated with both the term
"Nikon," as well as with the specific Nikon page of interest. The
intent vector is thus incremented at the entry corresponding to
"high-resolution." Similarly, other documents that are associated
to the terms in the context vector may be discovered based on the
community wisdom and are incremented in the intent vector. For
example, a Canon camera page may have strong affinities to "SLR
camera" and become part of the intent vector to some degree. The
affinities 604 that allow the expansion of the context vector into
an intent vector are determined by the affinity engine which is
described below and in detail in U.S. patent application Ser. No.
11/319,928, filed Dec. 27, 2005. In summary, the affinity engine
learns connections between documents and terms, documents and
documents, terms and terms, as well as users to other users,
documents, and terms by watching all of the implicit behaviors of
the user community on the site and by applying behavioral
fingerprinting and the use rank algorithm to determine interest and
associations. The ability to translate context into intent
effectively is a key aspect of the invention.
[0058] Step 3: Identify the group of users who share affinity to
the current intent, as well as those users who exhibit behavior
most like the current user within the context of that intent. In
U.S. patent application Ser. No. 11/319,928, filed Dec. 27, 2005,
these are called "experts" and "peers" respectively, but here we
combine them both under the name "peers." This peer group is
represented by a user vector 605 and each user entry in the vector
may have a weight indicating how strong of a peer he is to the
current user in this context. Although not generally recommended,
the system is also capable of limiting the peer group to those
users who match the current user based on a set of predefined
attributes, such as age, gender, location, or other demographic
variables. Similarly, the peer group can be limited to those users
who visit the site at the same time of day, e.g. morning,
afternoon, evening. In some cases these attributes can be used to
influence the peer weights for users, such as giving slightly more
peer weight to those users who also best match the user along these
predefined variables. The invention can also learn these weights by
comparing behavioral patterns, e.g. documents found useful in the
simplest case, within and across each of the predefined attribute
groups. Higher intra-group similarity in behavior compared to
inter-group similarity indicates the group is differentiated and
thus a higher weight of influence for groupness is warranted when
influencing the peer group. Similarity can be measured by looking
at similarity of documents and terms used through one of many
similarity calculations, e.g. cosine similarity, on users'
aggregated interest vectors.
[0059] Step 4: Look at which documents have highest affinity to the
identified peers within the current intent 606. To do this the
affinity engine looks at a combination of factors, including the
overall usefulness of a piece of content, as represented by the
activeness vector, learned affinities between terms and content, as
represented by term-doc matrices, and predicted navigational
patterns, as represented by next-step matrices, associated with
those peers the factors are weighted according to their peer
weight, and used in aggregate to compute those documents with
highest affinity to the current intent. These documents become the
unfiltered recommendations 607. Filtering, described in greater
detail below, may now be applied to limit or augment those
community recommendations.
[0060] Step 5: Now that the recommendations have been identified
and appropriately filtered, a final optional step is to ask the
affinity engine for community information on each of the
recommendations 608. This information can be combined with other
asset information, such as title, size, or price, and displayed to
the user to help them understand the community wisdom underlying a
recommendation. Many aspects of the community wisdom associated
with a document in the affinity engine can be exposed, but in
common implementations we expose the number of users in total who
found value in the document, the number of peers who associated the
document with the current context/intent, and the terms/phrases the
peer community has associated with the document.
[0061] This latter piece, i.e. terms associated to the document, is
called a virtual folksonomy because it represents terms that the
community has associated to the document, but unlike a traditional
folksonomy, discussed above, where users must explicitly make the
associates, the virtual folksonomy is created automatically by the
affinity engine based on the implicit actions of the user
community. Terms that are searched or clicked as navigation links
and lead, within one or more steps, to useful content, as
determined by behavioral fingerprinting, become automatically
associated. These term-doc connections are both fundamental to the
process of providing recommendations, as well as providing useful
feedback to users on the topics associated with each
recommendation. When these terms are displayed to a user, they can
be made clickable so that the user can click them to provide
further input regarding their current intent. This information then
becomes part of their context vector, the whole process repeats,
and new recommendations may be provided based on this new
information. FIG. 7 provides a conceptual diagram of a virtual
folksonomy, contrasted against traditional approaches for
connecting terms to content.
Time (Decay, Trend, and Fad Detection)
[0062] Time factors in heavily to all computations in moving from
context to recommendation. First, all information collected by the
affinity engine is subject to time decay. This means that, for
example, information from last month has less of an influence on
the calculations than information from today. This is important
because the site may change, e.g. new content added or removed,
people may individually change, e.g. their interests may change,
and the community as whole may shift interest, e.g. new fads.
Although there is a default decay rate for all information, some
information may decay away more rapidly if it is determined that
this is necessary. For example fad behavior, such as interest in
Christmas products that come and go quickly, may need to be decayed
more quickly to prevent recommending Christmas products too long
after Christmas has passed. To prevent this, the system runs a
trend detection system across all content on a periodic basis, e.g.
once a day, or every five minutes for very time-sensitive sites.
Trend detection can be done in a number of ways, but one way is to
use the Mann-Kendall algorithm. Other ways include various
regression techniques that employ least-squares fit. If a strong
negative trend is detected for a given piece of content, the
information associated with that content in the affinity engine is
decayed at a more rapid rate. The result is that the likelihood of
the affinity engine recommending this piece of content is
reduced.
[0063] Another algorithm for fad detection that is run on a
periodic basis is cyclical fad detection. If a piece of content
shows a strong positive trend and that same trend can be found at
regular intervals in the past, then that piece of content can be
automatically boosted in importance in anticipation of the coming
trend.
[0064] In a traditional Web site, even where the site proprietor is
trying to understand customer behaviors, techniques that are
typically used survey the customer and analyze the customer
responses to produce a report. By the time the report is analyzed,
the results of the report are out of phase with the actual
situation at the time the report is being reviewed. For example, in
a traditional system, a commerce site might collect feedback during
the Christmas season and redesign their site in February in
response to that information. In effect, the commerce site is
trying to sell Christmas products in February. Alternatively,
analytics reports may be created offline and analyzed by a team of
specialists to infer trends and determine appropriate actions; a
process which may take weeks or month and again lead to out-of-date
site modifications. In this way, the automatic nature of the
invention herein allows a Web site to adapt in real-time to
discovered trends and fads, providing recommendations to users that
fit the current context and time.
[0065] In addition to contributing to the process of automatic
content recommendation, however, the trend and fad detection
algorithms used by the invention can also be exposed to owners of
the content system, e.g. the Web site, through reports within a
customer portal. In the preferred embodiment, such reports on
community trends and fads can, for example, be used by merchants to
promote certain products or content at the right time, making such
promotions more effective. Given the example of products that are
sold during the Christmas season, if such sales were to die out
after Christmas, then the proprietor of the commerce site would be
able to follow the sales curve based on community interaction with
the Web site. If, on the other hand, an emergent news story drives
demand for a product quickly, the invention allows the merchant to
watch the curve of demand and respond in real-time. Thus, if people
are all of a sudden dramatically interested in a particular piece
of content or product, the proprietor of the Web site can provide
that content or product more quickly. If the demand dies out
quickly, for example the interest was based on a fad, then the
proprietor of the site can adjust to that fact as well.
Affinity Engine
[0066] As already mentioned, and as discussed in greater detail in
U.S. patent application Ser. No. 11/319,928, filed Dec. 27, 2005,
the affinity engine learns connections/affinities between
terms-documents, terms-terms, terms-users, documents-documents,
documents-users, and users-users. (Note: the terms document,
content, and asset are used interchangeably throughout this
document. In all cases, these terms refer to any type of medium
that provides information or services including, for example but
not limited to, webpages, word documents, pdfs, video, audio files,
widgets, and/or products). In the preferred embodiment, all
affinities are stored as sparse matrices and vectors. However,
there are many alternative ways of storing such information known
to those skilled in the art. Although there is ultimately a single
number that can be calculated to represent the affinity between any
two entities, e.g. a document and term, there are usually several
sub-affinities that are combined in a weighted sum to arrive at
that single number. The weights on that sum may be dependent on
context. For example, documents have at least three kinds of
affinities to other documents: similarity in virtual folksonomy,
i.e. terms the community has associated to the documents;
similarity in user groups, i.e. how many users have used both
documents; and similarity in navigational patterns usage, e.g. do
users show a pattern of finding value in one document after using
the other. A variety of mathematical techniques are available to be
employed as appropriate to each kind of data, although a
combination of vector space models and custom probabilistic
techniques are currently used by the preferred embodiment of the
invention. See U.S. patent application Ser. No. 11/319,928, filed
Dec. 27, 2005 for more discussion.
[0067] One important aspect of the invention is that all
sub-affinities are always computed bi-directionally. To illustrate
this bidirectionality, we describe its application to the
term-document sub-affinity derived from the virtual folksonomy.
FIG. 8 is an architectural diagram showing the aspects of the
invention that involve topic attraction and topic match, the two
dimensions that make up the bidirectional virtual folksonomy
connection between terms and documents, as well as general
activeness. In the example of FIG. 8, a query is provided 801 and a
percentage of respondents who ultimate find value in one of three
example documents are indicated. The query involves a particular
Nikon camera and it can be seen that 20% of the respondents find
value in a Web page focused on high-resolution cameras 802, 78%
found value in the overview page for the specific camera 803, and
only 2% of users making this query found value in the detailed
specification sheet for the camera 804. As a side note, remember
that value is determined by the full spectrum of behaviors
exhibited on a given page by the user (described in detail
later).
[0068] There are two ways to consider the virtual folksonomy
connection between the query and each example document. Topic
attraction starts from the query and considers the proportion of
users who searched for "Nikon 580X" and subsequently found value in
each document. In this example, the Overview page 803 has highest
topic attraction, with 78% of users finding value there, followed
by the High-resolution camera page 802 with 20%, and then the spec
sheet 804 with only 2%. Note that this example is simplified for
the purposes of illustration. In reality, we also break down the
query into its component parts and consider the affinities of these
to each document, as well as consider affinities between other
terms that the affinity engine has learned have similar meaning to
this one. In this way, topic attraction can be thought of as a
problem of predicting the probability that a user finds value in a
given document, given the intent represented by the query. In the
preferred embodiment arriving at this probability is accomplished
using a combination of probabilistic techniques, including Bayesian
inference.
[0069] Topic match looks at it from the other direction starting
from each document and considering all of the other terms that have
been associated with it through the behaviors of other users. So,
for example, the High-resolution camera page 802 may have many
other camera names connected to it and, thus, the degree of focus
on the particular query entered is lower than both of the other two
pages, whose term-document connections are more highly concentrated
around "Nikon 580X" specifically. In fact, the Spec-sheet 804 may
turn out to be the document with term connections most focused
around "Nikon 580X" and thus have the highest topic match for this
query, even though it has the lowest topic attraction. As with
topic attraction, in reality, topic match also breaks down the
query into its component parts and takes into account affinities
between other terms not in the query. In this way, topic match can
be seen as finding the document with the best overall match in
topic to the intent represented by the query. In the preferred
embodiment, a vector space model and modified cosine similarity
technique, similar to that used by traditional full-text search, is
used to determine the degree of matching.
[0070] These two directions of considering the affinity of each
document to the query, i.e. topic attraction and topic match, are
combined in a non-linear weighted sum along with the final factor
of overall activeness i.e., usefulness, to arrive at a value for
the virtual folksonomy sub-affinity. This sub-affinity is then
combined with other sub-affinities, such as those based on
navigational patterns, and filtered through the lens of peer
groups, to arrive at an ultimate ranking of documents against the
query, called UseRank. Similar bidirectional techniques are used to
compute UseRank when providing recommendations on a particular Web
page, and when considering the full context and intent vectors.
[0071] There are several methods for combining the various
sub-affinities and the bidirectional dimensions therein to arrive
at the ultimate UseRank of documents for a particular user and
context. In the preferred embodiment, one or more thresholds are
applied to each dimension, including absolute and relative
thresholds before they are combined together based on hybrid
arithmetic-geometric weighted average. Each resulting sub-affinity
is then similarity subjected to a thresholding before being
combined with other sub-affinities again based on hybrid
arithmetic-geometric weighted average. The values for the
thresholds are generally fixed, however, the individual weights for
the weighted averages can also be adjusted and learned based on the
success of the result set returned by the affinity engine.
Full-Spectrum Behavioral Fingerprints
[0072] As discussed herein, a key feature of the invention is the
processing and analysis of implicit observations that are made
during the actual use of a Web site, for example, versus
traditional approaches in technology which use explicit feedback.
One key advantage of the invention, as confirmed through scientific
studies, and as understood from human psychology and sociology, is
that humans are very bad at giving feedback, particularly if the
feedback must be given explicitly. If a person is being surveyed,
the person does not have an incentive to give actual accuracy in
the form of feedback. One aspect of the invention eliminates such
survey bias by using the implicit behaviors observed during use of
particular materials on the Web by individuals within a community.
Thus, the invention trusts what people do but does not watch what
they say. The invention watches people's behavior through their
actual action, implicit behaviors, and can accurately interpret
what their true intent is and whether or not they dislike or like
something. Thus, the invention observes behavior versus click
actions.
[0073] Current technologies focus on clicks, such as Web link
clicks. If a person clicks on a link, that click is reported. The
click may be reported as having resulted in a viewed Web page, even
though not much time is spent on the page on which the person
clicked. Thus, this known approach is not a good indication one way
or another if the page is good or bad. If a link is put in a
prominent position on a Web page, then people are likely to click
on it. However, when people get to the location indicated by the
link, they may immediately leave the site. This is why the number
one used button on the browser is the BACK button. The use of the
BACK button could indicate like or dislike of a site. Accordingly,
the invention recognizes that it is the action of the user after
the click that matters and not the click itself The invention
tracks behaviors beyond the click to determine whether a page is
good or bad. Thus, if a person backs out of the page, it is
considered negative feedback, i.e. the person did not like it. In
this way, clicks can identify a very negative reaction in the
invention. If a person goes to a link, follows the link down,
spends time there, and does other things, that behavior is tracked
as well. If a peer group validates the behavior as consistent, e.g.
a significant number of the group members exhibit the same behavior
when reacting with the page, then the page is considered to be a
good page.
[0074] An embodiment of the invention goes one step farther. Not
only does the invention determine which assets are useful based on
behavioral fingerprinting, but it also learns the context
associated with the usefulness. For example, the invention can
learn that a particular camera page is very useful for users that
show interest and intent in high definition cameras, but not if the
intent is compact cameras. In this way the affinity engine is able
to distinguish the usefulness of assets based on the context and
intent expressed by a user through their implicit actions.
[0075] The primary input to the affinity engine, which drives all
of the learned associations (affinities) is the behaviors of users
on the site. In the preferred embodiment, all behaviors are
captured by the ObserverTag, i.e. a piece of HTML/JavaScript
embedded within the Web site, typically in a header or footer
template. Although this is the preferred method for capturing user
behaviors, it is also possible to capture user behaviors using a
browser plug-in or lower-level network traffic analyzer. The
behaviors captured include:
[0076] Pages visited and in what order
[0077] Time spent on each page
[0078] Links clicked
[0079] Searches performed
[0080] Time spent scrolling on a page
[0081] Portion of page visible in the browser window, and for how
long
[0082] Page sub-elements opened/closed
[0083] Media launched, time spend viewing that media, and explicit
action taken on the media
[0084] Use of the back-button
[0085] Repeated visits to a particular piece of content
[0086] Mouse movement while on the page
[0087] Ads viewed and Ads clicked
[0088] Explicit actions, e.g. Add to Cart, Purchase, Email, Save,
Print, Print Preview
[0089] Virtual Print: content returned to frequently over the
course of several hours or days
[0090] Virtual Bookmark: content left open over multiple hours or
days with intermittent periods of activity
[0091] Entrance and Exit paths
[0092] The captured information is send back to the affinity engine
for processing. FIG. 9 shows the process for analyzing these
behaviors or other implicit data captured from various user
interface devices. The input to this process is the user trail 901
which includes all assets visited and the implicit (and explicit)
actions observed on those assets. There are two main steps in
processing the behaviors that combine to provide an understanding
of what content is useful and in what contexts. The first step is
determining whether a user is finding value (usefulness) in a
particular piece of content. Conceptually, the more time spent on
the page in think mode, i.e. user is processing the information,
the higher the likelihood that it is useful Think mode can be
approximated by non-scrolling time on a page, where some scrolling
or mouse-movement has been detected with a specified time range.
Repeat visits and percentage of page seen are also generally good
indicators of liking or usefulness. For each user and piece of
content, we can create a behavior vector 902 with each entry in the
vector representing one of the features listed above or a
predefined combination of features. However, not all content is
created equal nor are all users created equal. We normalize the
behavior vectors in a several ways. First, we normalize by user to
make behavior vectors comparable to the rest of the user population
903. For example, some users may read slower than others, affecting
their mean time spent on a page. Some users may use the mouse more
than others. One way to accomplish this normalization is by
translating all entries into z-scores which adjusts for means and
standard deviations specific to that user.
[0093] A second normalization is to normalize based on the content.
This is done based on inherent or specified properties of the
content. For example, 30 seconds spent on a one paragraph document
likely has a different meaning than 30 seconds spent on a ten page
document. Dwell time can thus be normalized based on page length or
number of words. Similarly 30 seconds spent viewing a 30 second
video has a different meaning than 30 seconds spent viewing a five
minute video. In many cases the Observer Tag is capable of
capturing these page characteristics through information within the
DOM (Document Object Model). However, when not available in the
DOM, the invention provides other mechanisms for collecting the
needed information from offline catalogs, e.g. a product or media
catalog, or allowing the Web site designer to add explicit
information in the page itself, e.g. as meta-tags or added
JavaScript variables. For example, on an e-commerce site, certain
pages may be defined as information versus product pages. The
system can normalize the behavior vectors for each of these content
groups independently. In this way, the dwell time necessary to
indicate liking can be adaptable to the type of content. All
behaviors (features) can be made adaptable in this way. In the
current implementation, these normalization strategies are
hard-coded. However, the invention allows the possibility for
plugging-in various machine learning techniques to learn the
appropriate normalizations.
[0094] We now have a behavior vector normalized to the user and the
content 904. In the current system, a predefined set of rules are
applied to determine whether this behavior vector represents
liking/usefulness. Each of the normalized features, in turn, is
considered to determine whether it meets the pre-specified
thresholds for indicating usefulness. Each passing feature
increases the probability of usefulness 905. In some cases, the
thresholds for a given feature are pre-specified and tuned by the
person implementing the system based on previous experience. In
other cases, these thresholds are dynamically determined by the
system. For example, certain features are known to exhibit a
bimodal distribution and the threshold can be dynamically
determined to lie between the two modes. The invention also
provides a mechanism for plugging-in various machine learning
techniques to learn which features are most important, and thus
dynamically learn the rules for converting from the behavior vector
to the probability of usefulness.
[0095] Once usefulness, or probability of usefulness, has been
determined, the second step in the analysis of behaviors is to
understand the entire context surrounding the use of that piece of
content, including searches done prior to the use, links clicked
prior, and pages used prior. All of this information combines with
information about the user to influence affinities 906 (affinities
can be learned and stored in a number of ways, described in more
detail in U.S. patent application Ser. No. 11/319,928, filed Dec.
27, 2005). Before doing so, however, there is one final step:
validation. Validation is a form of noise filtering wherein an
affinity connection is established, e.g. between a document and
term, if and only if enough similar users have, through their
behaviors, confirmed this connection. One user making the
connection is not enough for an affinity to emerge. A minimum
number of users in the same context (peers) must have exhibited
similar behaviors and connections for that affinity to be
validated. We call this peer-validated behavior.
UseRank.TM.
[0096] UseRank.TM., the ultimate ranking of the usefulness of
content based on a user's context and intent, based on learned
affinities and full-spectrum behavioral fingerprinting, can be
compared to the very popular PageRank strategy made famous by
Google. In PageRank, each Web page is given a value based on the
number of other pages linking to it. In addition, links coming from
pages that are themselves of high value raise the PageRank even
more. This democratic strategy is quite effective in getting users
to useful Web sites based on their Google query, but generally
breaks down once the user begins to look for further information
within the Web site itself The main reason for this is that pages
within a Web site, particularly those down into the long tail of
content, are not heavily linked to either externally or internally.
Internally, the linkages are determined solely by the structure of
the Web site and more links does not necessarily mean more value.
In addition, many of the pages on such sites are in formats other
than HTML, e.g. PDFs, Word Docs, or videos, and do not link to
other content at all.
[0097] The UseRank methodology, based on user behaviors on the Web
site alleviates all of these problems. Instead of relying on the
linking of documents by Web designers, it relies on usage of
documents by Web users, who are a rich source of information on any
Web site. Usage is a truly democratic way of learning what content
is most valuable. In fact, the designers of PageRank recognized
their approach as an approximation of user activity. Google's
choice to approximate user value based on PageRank makes sense
given the privacy concerns associated with tracking a user's
behavior across the entire Web. In the preferred embodiment of
UseRank, we track only the behaviors of user's on sites
instrumented with the Observer Tag, and always in an anonymous
fashion unless otherwise configured by the Web site deploying the
Observer Tag.
Long-Tail
[0098] Another important aspect of the invention emerges from the
context-driven approach underlying the affinity engine. Many
previous systems that are content-centric or user-centric suffer
from a problem where only the most popular product/content is ever
recommended. Because these systems lack a deep understanding of the
user's current context/intent, this is the best that they can do.
The invention provides a context-centric approach that allows the
affinity engine to narrow its focus to the subset of users (peers)
and content that match the current user's context/intent, even if
that intent is not particularly popular in the grand scheme of
things. This is what is known as the long tail of the products,
i.e. those products that may not be the most popular overall, but
are extremely important because they strongly meet the need of a
small but important subset of the community (see C. Anderson, The
Long Tail: Why the Future of Business Is Selling Less of More,
Hyperion Press (2006)). The invention is capable of identifying
that long-tail interest along with the associated peer group and
related content and can thus recommend those important long-tail
products/content at the appropriate time (see FIG. 10). This is
critical for product/content providers because it is often these
long-tail products that are most useful to the community and often
lead to the highest margins or benefits for the company itself
[0099] Thus, the invention uses like-minded peers as an enabler to
target small target segments along the long tail, instead of using
individual problem personalization and historical interest as known
in the prior art. The invention provides contextual targeting.
People's needs within a given context are typically similar among
like-minded people. An insight of the invention is that people have
hundreds of profiles, if not thousands of profiles. These profiles
have no cross connections to reality, so it is difficult to predict
an individual's use level without context. Once the individual is
in a particular context, i.e., among like-minded peers, the
individual behaves similarly to everybody else within the group
context. Thus, if a person is visiting a golf equipment site to buy
a golf driver or clubs, the person behaves in a manner similar to
other golfers. It does not matter what the person's political bias
is, or their cultural background. These aspects of the individual
have no relevancy because the person's present context, which is
based upon group membership, is more relevant.
Wisdom of the Crowd
[0100] The invention represents a new approach to leveraging
implicit community wisdom to create adaptive Web sites and other
information portals. In his book, The Wisdom of Crowds, James
Surowiecki explains how the collective intelligence of a large
group of average individuals almost always outweighs the
intelligence of experts. To illustrate the concept, he uses an
example from a county fair, where a group of fair attendees
attempted to guess the weight of a cow. A group of so-called
experts, e.g. butchers, dairy farmers, etc., also made their guess.
In the end, the experts were all off on their guesses by a large
amount. The average weight guessed by the crowd of non-experts,
however, came within one pound of the actual weight of the cow.
Surowiecki goes on to show how a similar phenomenon can be seen in
everything from stock market prediction to democratic governance.
This notion that groups of individual actors can collectively
exhibit a level of collective intelligence going beyond even the
sum of the individual actors themselves has been known in the
fields of biology and artificial intelligence as emergent behavior
and collective intelligence. Numerous examples exist in nature
where very simplistic individual animals, such as ants or bees, in
collection exhibit extraordinary intelligence and resourcefulness
in meeting the needs of group, such as finding food or building
nests.
[0101] In the context of Web site design, Surowiecki's experts are
the Web designers. These experts attempt to make correct decisions
on which Web pages should be linked to what others, such that
visitors are best able to find what they are looking for. Web
designers may also spend a lot of time an effort tuning search
results to match the expected needs of their visitors. The crowd in
this context is the large group of Web site visitors who come to
the site. Generally, the crowd has no direct impact on the
organization of the Web site. They remain silent.
[0102] The invention gives this crowd a voice, enabling their
collective actions, i.e. a form of expressed opinions, to be
collected and automatically drive decisions on Web site
organization through their impact on recommended content. The
invention thus taps into the wisdom of the crowd for the purposes
of creating useful Web sites. Although in the preferred embodiment,
the impact of crowd wisdom is sectioned off into specific regions
of recommendations and social search within the Web site, it is
easy to imagine an extended implementation where the entire
organization of the Web site, all of its links and menus, for
example, are ultimately driven by and adapted to the behaviors of
the Web site visitors. In this way, the entire Internet made up of
multiple interconnected sites could begin to evolve and adapt into
a form that best meets the needs of Internet users.
[0103] FIG. 11 provides a Wisdom of Crowds pseudo-equation showing
how context/intent can be incorporated in a generalized way to
extend the wisdom of crowds concept, as exemplified by an
embodiment of the invention. In this pseudo-equation, each user's
implicit vote is weighted according to the similarity between the
current user's context and the context in which a user made that
vote. This equation could be further extended to incorporate
similarity between users. When all contexts are identical, this
equation correctly reduces to a simple average.
Memory Prediction Machine
[0104] The actions of the Affinity Engine can also be considered as
a form of Memory Prediction Machine. Recent learnings from
cognitive science teach that the brain is structured to capture and
encode associations between and among objects and concepts in
targeted regions of memory, and organize those associations in a
hierarchy moving from abstract to detailed. New stimuli are
responded to and their consequences predicted, based off of the
memories created from previous stimuli and learned associations
thus encoded. The Affinity Engine similarly learns associations
between users, objects, and contexts by observing interactions
between them in the environment of Web sites and remembers those
patterns in its memory, stored hierarchically. When a user exhibits
a context that has been previously learned by the Affinity Engine,
it lights up the appropriate associations within memory which
triggers the prediction of those objects that best meet the needs
of the current user and context. FIG. 12 is an architectural
schematic of the Affinity Engine in its function as a memory
prediction machine.
Seamless Integration with External Systems
[0105] An important aspect of the invention is its ability to
integrate seamlessly with other recommendation systems, policies,
and information sources. Some examples are shown in FIG. 3.
Search Engine
[0106] When a user performs a search on a site implemented with the
invention, the search terms are sent to the affinity engine for
processing. The affinity engine determines the set of content that
is most useful given the context of that search, based on learned
affinities from past community behaviors. However, the affinity can
also take into account opinions from external sources, such as a
search engine. The search engine 301 has its own set of
recommendations which the affinity engine can accept. The relevance
ratings from the search engine can be combined with the information
embodied in the affinity engine to produce a single unified set of
recommendations (discussed more fully in U.S. patent application
Ser. No. 11/319,928, filed Dec. 27, 2005). An XML feed is a typical
way for the system to interface with a search engine.
Product Catalog
[0107] The invention also provides a mechanism for automatically
importing product or media catalogs 305. The preferred means for
doing so is using an XML feed that is accessible to the system.
There are three ways in which the catalog can be used:
1) Information from the catalog may be displayed along with the
recommendations, e.g. summary or price; 2) Attributes from the
catalog may be used to filter the result set, e.g. the Web site
designer may want to restrict recommendations to products only or
PDFs only a particular section of the Web site; 3) Content
categorizations can be used to group content for the purposes of
normalization in the behavioral fingerprinting process described
above.
Ad Servers
[0108] Another aspect of the invention is that it is
content-agnostic. The system can observe behaviors on and recommend
Web pages, pictures, videos, documents, blogs, downloads, and even
ads. In the case of ad recommendations, it is often necessary to
integrate with an ad server. Ad servers 303 can provide
functionality similar to both search engines and catalog systems as
described above. The invention interfaces with ad servers in the
same way as it does to these, typically through XML.
Merchandizing Systems
[0109] Merchandizing systems 304 in the general case provide a
mechanism for the Web site owners to influence what
products/content are recommended to users. In the invention, it is
preferred that the community be the primary driver of
recommendations. However, there are times when the owners have
specific needs that must be met independent from the community's
expressed interest. For example, the owner may choose always to
recommend a particular product or piece of content first in a
particular context, for example, there may be a promotion going for
a product. Owners may also wish to influence the community to
purchase products with higher margins, or prevent a certain piece
of content from being recommended. There are two ways in which the
invention can honor such influence. The first is through a custom
rule interface that is one component of the invention. Here, Web
site owners can log in and choose from a variety of predefined rule
types including pinning, i.e. forcing a particular recommendation
to show up; blacklisting, i.e. preventing a given recommendation or
class of recommendations from showing up; and boosting, i.e.
increasing the chances that a recommendation or class of
recommendations show up, but still honor the community wisdom. All
of these rule types can be applied globally or only in a given
context, e.g. on a particular page, for a given search term, for a
given class of users, in a particular time range. Another type of
rule allows owners to honor fully the community wisdom, but
influence the goal of the recommendations. For example, rather than
focusing on recommending useful products, the affinity engine can
be told to recommend products that a user is most likely to
purchase. As another example, the affinity engine can be asked to
recommend content that most likely leads users to a particular set
of target pages. In addition to the custom rule interface, the
invention provides a mechanism for importing rules in XML form from
an external merchandising application. When rule types exist in the
external system that do not already exist within the invention, a
custom rule plug-in can be designed for the invented system which
matches the desired behavior of the external rule type.
User Profiling
[0110] A further type of system that the affinity engine can
integrate with is user profiling systems 302. In its simplest form,
a profiling system is a set of attributes, e.g. demographic
attributes, associated with a user. These attributes can be passed
along to the affinity engine on a recommendation request. As
discussed earlier, the affinity engine can take these attributes
into account when identifying a peer group, which then influences
the recommendations that are returned. More sophisticated profiling
systems require XML integration similar to other external
components. Here, the affinity engine may have to dynamically
contact the external profiling system during the peer
identification process.
Preferred Embodiment
[0111] In the preferred embodiment of the invention, a
Recommendation System is implemented in a Software as a Service
(SaaS) Model to provide automatic suggestions that help Web site
visitors find products or content they like or need. FIG. 2 is an
architectural schematic diagram of a method and apparatus for
context-based content recommendation. In FIG. 2, interaction of
users with a Web site produces implicit emergent behaviors. Such
user interaction includes page referrals, links, entry trails,
queries, page sizes, mouse movements, peers, negative experiences,
virtual bookmarks, time spent, virtual printing, exit trails, and
the like. Such behaviors are processed in a recommendation/affinity
engine according to the invention, resulting in automatic content
and product recommendations in the form of social search and
navigation guides, as well as providing real-time feedback to a
merchant such as regarding visitor clubs, and identifying content
gaps (each of which is discussed in greater detail below). The
following key processes comprise the recommendation system:
[0112] Capturing Implicit Behavior--The implicit Web site behaviors
201 that serve as input to the recommendation system are captured
client-side by small snippets of JavaScript code embedded within
Web site pages, e.g. through a Web site template. The behaviors are
then sent to the remote recommendation engine where they are
processed using the full-spectrum behavior fingerprinting
technology described herein.
[0113] Distilling Collective Wisdom--The recommendation/affinity
engine 202 processes all incoming information to identify emerging
intent among Web site visitors and captures the collective wisdom
of the crowd. The recommendation engine identifies and learns
affinities between and among users, content, and terms, identifying
consistent patterns of behavior and removing noise. The affinity
engine also recognizes and adjusts in real time to changing fads
and trends, as well as cyclical patterns of behavior, such as
seasonal patterns, and other shifts in user interest.
[0114] Delivering Recommendations and Search Results--When a
visitor arrives at a page, the recommendation engine is passed
information about the users current context, either by snippets of
JavaScript on the client, called Recommendation Tags, or by the Web
site server, and a request is made for appropriate recommendations.
The recommendation engine translates the user's context into intent
using the technology described in this application, identifies the
appropriate group of peers, and ultimate returns with a set of
content recommendations 203 ordered based on the their computed
UseRank.TM.. These recommendations are then displayed within the
Webpage on the user's browser. These recommendations may take the
form of navigation links or enhanced search results. When
delivering recommendation results, the recommendation system may
additionally contact other external services, such as the Web
site's full-text search engine, product catalog, or an ad server
(see FIG. 3).
[0115] Customer Portal--In the preferred embodiment, owners of the
Web site also have access to information and patterns learned by
the recommendation engine through a customer portal called Insights
204. The customer portal provides the ability to configure the
recommendation system and add merchandising rules, as well as view
a set of reports providing information on the usage of the Web site
by the community and the patterns of behavior and affinities
learned by the system.
[0116] FIG. 13 shows a screenshot of the Customer Portal homepage
in the preferred embodiment. Many different reports and
configurations are accessible using links within this portal. For
example, the invention allows for product/content gap detection,
where gaps are detected based on usage patterns on the site and
site owners are helped to introduce new products or content in
those areas where the gaps are detected. As another example, the
Customer Portal also provides the Web site owner with a set of
reports to track the ongoing value that the Recommendation Engine
is brining to the Web site. FIG. 14 shows a screenshot of an A/B
report that tracks the revenue lift due to the presence of
recommendations on the Web site. The inventors have consistently
seen revenue improvements of 20-30% or more. Other lift reports
available within the portal include improvements in page views,
engagement time, site stickiness, average order value, and user
conversion to specified "business targets" within the Web site.
Applications
[0117] The following discussion involves different applications of
the foregoing technology:
[0118] FIG. 15 is a screenshot showing community-guided e-commerce.
In FIG. 15, five different capabilities of product recommendations
are presented:
1) Comparative Products, also known as Similar or Competitive
Products, are shown for purpose of comparison shopping and
up-selling based on affinity of like-minded peers on their product
considerations, i.e. observed full-spectrum behavioral finger
prints discussed earlier. Consideration-based peer recommendations
promote higher end/niche products, and yield more revenues and
profits for a site. 2) Affiliated Products are also shown for
cross-selling related/non-competitive products such as accessories
to increase overall order size. 3) The invention also provides a
showing of most popular products, such as products with greatest
appeal across the whole site or within a category. 4) The invention
also generates intent-driven landing pages, also known as AdGuide
and Site Concierge, with recommendations based on what a visitor
searched on standard Web search engines such as Google, Yahoo or
MSN. If a visitor search "Viking Cooktop" on Google and landed on a
customer site, AdGuide serves the best Viking cooktop
recommendations to the visitor on the landing page dynamically
instead showing him something irrelevant. 5) The invention also
provides a community filtered search on products based on the
foregoing.
[0119] For peer-driven recommendations, the invention automatically
identifies small and large population segments having a unique
interest and guides individuals to popular, competing, and
accessory products. The invention provides a user intent and
product mapping that translates shoppers' intent into
peer-validated products and brands. The inventors have found a 700
percent conversion power based on independent studies. The
invention provides fad and trend detection and recognizes and
adapts to seasonal, promotional, and other shifts in shopper
interest in real-time. The invention also allows for product gap
detection, where product gaps are detected and site owners are
helped to introduce new products or content in those areas where
the gaps are detected. The invention assists in merchandising with
real-time customer feedback. It works with existing merchandising,
promotions, segmentation and search, and magnifies products or
product families. The invention provides built-in concurrent A/B
measurement and reports in real-time the net revenue generated by
the invisible crowds.
[0120] FIG. 16 is a screenshot showing community-guided product
search with a travel example. In FIG. 16, the user has queried for
hotels in New York City. As a result of similar users of this Web
site for similar purposes, and by observing how the users have made
use of the information returned, recommendations are made to the
person with regard to hotels to select within New York City. The
search results are far more effective and relevant based on
like-minded peer interests and intent instead of showing many
irrelevant results by simply matching keywords or metadata, in this
case "New York Hotels".
[0121] FIG. 17 is a screenshot showing community-guided marketing
and online lead generation. As with the e-commerce application,
Five different capabilities are provided to a marketing Web
site.
1) Aspects of this feature of the invention include a social search
which involves UseRank.TM. and implicit learning. This approach is
intent-driven, adaptive, and makes use of an implicit folksonomy,
community terms such as link-text they use and search queries they
enter, as discussed above. The invention supports audio, video,
binary code, and all other content types because it does not have
to parse the content itself, whereas a typical search engine has
to, therefore is limited to text content and metadata only. 2) The
invention also provides Related or Similar Content based on the
implicit behavioral feedback of like-minded users. The invention
ranks the recommendations by usefulness and on-target-ness, based
on the context and intent. 3)
[0122] The invention also provides a most popular category, which
provides content with the greatest value across the whole site or
sub portion of the site. This promotion of information leads to
business target conversion increases such as
trial/download/registration conversions and may be provided at a
site or topic level. 4) A Next Step feature is provided that
concerns common next steps that lead to customer conversions. The
next step may be any next step in a business process or a natural
next set of content to read in connection with the process. 5) A
similar Intent-Driven Landing Page, as with the e-commerce
application discussed above, is available for a marketing Web
site.
[0123] FIG. 18 is a screenshot showing social search according to
the invention. In the example, the search is made for storage
devices. Users are provided information with regard to whether the
information is on target and for how many visitors, and popularity
rating and a community rank, i.e. UseRank.TM.. An on-target value
is also provided along with the results. Further, the results are
sorted by usefulness, based on information within the affinity
engine. The original keyword rank from IR or full-text search
engine is also provided as a before-and-after comparison. In this
example, if the UseRank.TM. is not used, this good piece of primary
storage product information would have been buried as rank 87, i.e.
the seventh result on the ninth search result page, and no one
would be able to find and use it. The UseRank.TM. has moved it to
the third position on the first page in the search results. At the
interface level, a Raw Results link is provided to give the users
the original results if this invention were not used there. This is
another way to see before-and-after impact of the UseRank.TM..
[0124] FIG. 19 is a screenshot showing community-guided online
support to promote self-service and satisfaction among the customer
base. The page shows top issue resolutions, including most helpful
articles, a top FAQ, and help forum, and blog articles. The page
also shows related help, including in-depth articles on the
subject, alternative solutions and, finally, reminders. Further,
common next steps are provided, including natural next steps for
resolution, common downloads, and additional support information
and contact information. Finally, the invention shows social
search, which is a key aspect of this embodiment of the invention,
based on UseRank.TM. and implicit learning. Thus, social search is
intent-driven and adaptive, makes use of the implicit folksonomy,
and supports all content types. Other good support applications for
this use include knowledge management (KM) portals, community
forums and discussion threads, chats, wikis and developer's
networks, etc.
[0125] FIG. 20 shows a community-guided intranet and knowledge
portal. In this example, social search is used within an intranet.
The search returns related pages, which include similar pages on
the topic, the pages are proven by the past experience of peers,
and serendipitous discovery is enabled. Further, the search filters
out low value content. The invention also provides most popular
search results, which includes content with the greatest value, top
applications within the company, and information specific to a
department or cross-company information. Finally, the invention
provides next steps, including where people go from this page to
meet their goals, a next step in the business process, and the next
set of content to read on the topic. Extranets, such as
physician-patient portals, dealer net, customer portals, are other
applications examples.
[0126] FIG. 21 shows recommendations implemented on a media site,
and FIG. 22 shows social search implemented on a media site. FIG.
23 is a screenshot showing community-guided media and cross-site
contextual recommendations. Similar Articles and Blogs are
recommended based on similar contexts. If you are reading a Tiger
Woods golf story, three other related Tiger news, based on other
similar peer interests are promoted to the same visitors. The
visitor gets more content and satisfied. The site get higher CPM
and CPA, both of which are current industry measures of visitor
conversion rates and site value from advertising perspective.
UseRank.TM., also known by Baynote as BrandRank.TM., can also be
used to re-rank the ads and product on the media, instead of
placing ads or products randomly or based on keyword match. Ads
validated by peers have significant higher CPM and CPA values, and
therefore increase revenue for the media sites. In addition, the
information contained in these various sources is applied to the
affinity engine, which discovers cross-site affinities among the
various sites. The result is to provide targeted content and
products.
[0127] FIG. 24 is an example of the invention being used to
recommend video content. As discussed in this application, the
invention is content agnostic and is able to learn affinities
between term, users, and content based entirely on implicit user
actions.
[0128] FIG. 25 shows a screenshot of an Implicit Topic Cloud.
Another application of the invention is to collect and display the
community vocabulary or terms. On the surface, the Topic Cloud may
appear to be similar to explicit tagged cloud such as Deli-cio-us,
social bookmarking, etc. But the key difference is the
implicit-ness. Implicit Topic Cloud is built based on the actual
user's activities on the site, i.e. the queries they entered and
link-text they used. Actions speak louder than words. The implicit
Topic Cloud reflects in the interests of 100% of the site visitors,
not the few loud visitors who are willing to tag things. The silent
majority is the key to collect true wisdom of the community.
[0129] FIG. 26 shows an architectural view of an integration
between the invention and an AdServer. To serve the best ad to a
user, the integrated system first contacts the existing Ad Server
to get a list of acceptable ads to display to the user given
demographic or other information. This list is then sent to the
Affinity Engine which, based on learned affinities between the Ads,
users, and context, chooses the best ad to display to the current
user. This allows the ads served to be appropriate both to the
user, as well as the user's current context and intent.
[0130] There are many ways to leverage the community wisdom
distilled by the Recommendation Engine, in addition to social
search and onsite content/product recommendations. Many
applications can be built or modified to directly tap into this
wisdom through APIs to the Recommendation Engine. FIG. 4 is an
architectural diagram showing the invention's role as a community
wisdom platform. The community-wisdom platform can also include
such items as contextual email marketing, mobile applications, IPTV
systems, SEO/SEM applications, and mashups in custom applications.
Within these systems, input from the Recommendation Engine can be
used to dynamically determine the most appropriate information
and/or organization of information to present to users. Such a
platform is built upon Web services that include such features as
AJAX and REST (discussed below). Thus, the invention is built upon
new Web 2.0 fundamentals, which include a true understanding of
invisible crowds, gathering information from like-minded peers,
making content or product recommendations, providing onsite social
search, implicit community-based reports, and the like. The
following descriptions give details on how they are deployed:
[0131] Email Recommendations: Marketing uses massive emails to
communicate with customers and prospects. But the content of the
email campaign is typically determined by the marketing staff The
invention enhances the quality of email campaigns by crowd-sourcing
the like-minded peers. The content of the emails are determined by
what's popular or similar to what the like-minded readers of the
emails are interested in. Marketers simply facilitate the process
of letting one customer promoting content to another customer
implicitly.
[0132] Mobile Recommendations: The quality demand of mobile
recommendations is even higher than the Web due to the small
footprint of mobile devices, such as cell phones and personal
devices. The invention's like-minded peer and intent driven
approach is better than traditional approaches based on
collaborative filtering, page views, and demographic or purchase
data. Intent/context driven peers predict people's need far better
than the competing approaches.
[0133] IPTV Recommendations: Similar recommendations are effective
for using Digital TV/HDTV to watch internet delivered movies and
video programs. With thousands of movies and TV program selections,
features like "You may also like these movies" are extremely
important for up-sell and cross-sell movies and TV programs.
Context-driven peer recommendations are the most effective way for
the viewers to like the recommendations.
[0134] Live Connect: FIG. 27 shows an architectural view of the
Live Connect system. Because the invention can identity like-minded
peers, in addition to harvest their collective wisdom implicitly
and to make peer recommendations to individuals, the invention can
connect the individuals with the like-minded peer group and have
them exchange information, knowledge, and experience. This is
similar to the real world experience of like-minded peers who may
not know each other. Imagine you are in Best Buy shopping for a TV,
you see other like-minded peers also shopping for TVs, you can ask
them questions for their experience of certain products.
[0135] SEO/SEM Recommendations: The invention is also used for
guiding Web site visitors at the moment of landing on the site. The
features are called "AdGuide," "Site Concierge", and "Site
Maitre'd." This application examines the queries that people have
searched on Google, Yahoo, MSN, ASK, and other major Web search
engines, then uses them as the proxy of user intent to display the
right product or content for visitors when they land on the site,
either on the pre-designed landing pages (SEM), or natural search
result pages (SEO). For example, if a visitor search "Viking
Cooktop" on Google and landed on a Baynote customer site, AdGuide
serves the best Viking cooktop recommendations based on collective
interests of like-minded peers to the visitor on the landing page
dynamically instead showing him something irrelevant or less
important by simply matching keywords.
[0136] Based on the affinities learned by the affinity engine, the
invention is also able to provide a Keyword Recommendation system
that provides suggestions to marketers on which keywords should be
purchased on external search engines as part of their SEO/SEM
efforts. For example, the invention can suggest those terms that
the community uses most often to describe content on a Web site, as
well as those terms which are most likely to lead to useful content
within the site. Also, given a particular collection of keyword
already purchased, the invention can suggest other words used by
the community that have high affinity to the existing set. These
recommendations can be provided to SEO/SEM merchandisers either
through reports within the Customer Portal or can be directly
integrated with external SEO/SEM systems to bid on the recommend
keywords dynamically and automatically.
[0137] Blogs, Forums, and Discussion Threads Recommendations: The
invention is used by community generated content such as blogs,
forums, discussions, pod casts, video content, etc. Because of the
volume of content involved, surfacing the right content in the
right order is even more important than expert-generated Web sites.
Using like-minded peers for recommendations and social search is
extremely important and effective. These Web sites can be public
facing blog sites, or login-required sites such as partner portals
or patient-physician portals, or developer's networks, or
intranets. Features such as "Similar content," "Accessory
products," "Next steps," "Most popular" and social search are very
useful and effective.
[0138] Insights (Visitor Clubs, Content Gaps, etc.): Insights, the
Customer Portal, has already been discussed in part earlier in this
application. Although a primary use of the invention is as an
automatic recommendation system, there are times when site
administrators may want to modify the behavior of the
recommendation system or see reports based on the knowledge learned
by the affinity engine. Insights currently offers three main
categories of functionality though its UI: configuration,
management, and reports. Configuration enables site administrators
to configure fully all aspects of the recommendation engine
required for full functionality and deployment, including
integration with external sources of information such as full-text
search engines and ad servers. Management enables site
administrators to modify the behaviors of recommendations and
social search in a holistic manner or specific to defined contexts
e.g. a particular search term or page location or user. For
example, administrators can create product/content promotions to
override community wisdom for set periods of time, blacklist
particular products/content from being recommended, artificially
magnify or boost defined classes of products/content, or set up
business rules such as limiting the classes of
products/recommendations acceptable for recommendation in
particular contexts. Reports offer various views on the community
wisdom distilled by the affinity engine. Although the reports
include a subset of the click-based and purchase-based information
found in traditional analytics, they go beyond these in providing
many details on the community wisdom distilled through
full-spectrum behavioral fingerprinting and the connections learned
by the affinity engine. Example reports are discussed below.
[0139] The Content Gap report uses the affinity engine to analyze
those user interests that are not being met by the existing content
on the site. Using the full-spectrum behavioral fingerprinting
technology, and by analyzing community behaviors as discussed
earlier in this application, the affinity engine can identify
community interests. The affinity engine can then analyze all cases
in which a particular interest was expressed by a user and the
subsequent behaviors of that user, including use of content and
level of value extracted, as well as subsequent searches or exits.
If a large portion of the user population with a particular
interest is unable to find useful content, then that interest is
designated a content gap. The degree to which the gap is suspected
by the affinity engine can also be reported. The administrator can
then take necessary actions to remedy the gap by adding new content
and then continue to use the Content Gap report to see the effect
on the presence of the gap.
[0140] The Visitor Clubs report provides the most comprehensive
view on the affinities learned by the affinity engine. Visitors to
the site are implicitly grouped based on shared interest. Each
interest group, or visitor club is displayed in the interface along
with the number of visitors in the club, their overall activity
level, the virtually folksonomy of terms that describe the club,
and the content/products which are most useful to that club in the
context of the club's interest. In addition, all club
characteristics can be charted over time to gain an understanding
of how the interest is trending over time. Clubs can also be
compared to one another for overlap in membership. This report
provides administrator with the ability to explore the learned
associations between users, content/products, and terms/contexts.
Such associations can then drive promotions or other merchandizing
activities within the Management section of Insights. The visitor
club information can also be used to drive various business
decisions outside of the context of the invented system.
Architecture
Recommendation Server
[0141] FIG. 3 is an architectural schematic diagram showing
affinity engine integration according to the invention. In FIG. 3,
a current user exhibiting an observable behavior within a
particular content site, such as current location and/or search,
based on previous action, or user properties, accesses the affinity
engine, which includes assets/products, users, and terms, all
arranged in groupings based on affinities. The user is provided
with asset recommendations in connection with products, categories,
information, media, and the like. The affinity engine is populated
by site owners who provide administrative input and exert
administrative influence. The information provided by XML/direct
input. The administrative input and influence filters, constrains
and seeds the affinity engine, applies merchandising rules, and
expresses goals, for example, likely use or likely purchase of a
particular product. Asset information is also provided in the form
of XML information, for example, a product catalogue or a full-text
search facility. The affinity engine collects implicit observations
from the visitor community to make such asset recommendations to a
current user. These implicit observations include asset actions,
such as time spent on a page, scrolling, mouse movements or clicks,
page interaction, and virtual printing or bookmaking; asset
navigation and asset navigation patterns, including, for example,
entrance path, exit path, links used, repeated visits, and
frequency visits, use of a back button, and recommendation use; and
visitor-community searching, including terms entered and/or
reentered, results clicked, results used, and subsequent
navigation.
[0142] FIG. 28 is a block schematic diagram showing the system
architecture of a preferred embodiment of the invention. A more
detailed discussion of various aspects of this architecture is
provided below. To understand FIG. 28 in greater detail, reference
may be had to Applicant's copending U.S. patent application Ser.
No. 11/319,928, filed Dec. 27, 2005, which is incorporated herein
in its entirety by this reference thereto, and which describes, in
greater detail, a preferred system for use in connection with the
invention herein.
[0143] The architecture consists of a server farm 20, a customer
enterprise 22, and a user browser 21. The user browser is
instrumented with an extension 23 and accesses both customer
servers 25 at the customer enterprise 22 and the server farm 20 via
a load balancer 27. Communication with the server farm is currently
effected using the HTTPS protocol. User access to the customer
server is in accordance with the enterprise protocols. The browser
extension 23 is discussed in greater detail below.
[0144] Of note in connection with the invention is the provision of
a failsafe. The extension 23, as well as the enterprise extension
24, are constructed such that, if the server farm 20 does not
respond in a successful fashion, the extension is shut down and the
enterprise and browser interact in a normal manner. The features of
the invention are only provided in the event that the server is
active and performing its operations correctly. Therefore, failure
of the server does not in any way impair operation of the
enterprise for users of the enterprise.
[0145] As discussed above, an extension 24 is also provided for the
enterprise which communicates with the load balancer 27 at the
server farm 20 via the HTTPS protocol.
[0146] The enterprise also includes a helper 26 which communicates
with the server farm via an agency 31 using the HTTPS protocol. The
agency retrieves log information from the enterprise and provides
it to log analyzers 28, which produce a result that is presented to
the usage repository 29. The agency also communicates with any data
sources accessible to the enterprise for the purposes of loading
information into the affinity engine 32. Such information includes
catalog database information, use profile data, authentication
data, as well as any other form of data that may be useful for the
purposes of determining or filtering recommendation and search
results.
[0147] Information is exchanged between the affinity engine 32 and
the browser and enterprise via various dispatchers 30. The browser
itself provides observations to the server and receives displays in
response to search or recommendation requests therefrom.
Alternatively, the customer server 25 can directly request search
or recommendation results directly from the affinity engine 32
through the Baynote extension 24 using REST.
[0148] A key feature of the invention is the affinity engine 32
which comprises a plurality of processors 33/34 and a configuration
administration facility 35. During operation of the invention, a
form of information, also referred to as wisdom, is collected in a
wisdom database 36.
Client-Side Integration
[0149] The preferred embodiment of the Baynote extension 23 uses a
custom-build tag-based AJAX-architecture for collecting Website
behaviors from the client as well as serving recommendations to the
client. FIG. 29 provides code snippets for JavaScript integration
with a client. FIG. 30 is a block schematic diagram showing an AJAX
tag platform according to the invention. In FIG. 30, a Web page is
displayed on a client and includes a particular script. In this
example, all files and results from the system are dynamically
injected into the DOMS as <script> elements. A coordinator on
a trusted Web server provides such things as a failsafe mechanism,
common code, a policy, handlers, and communication facilities. A
system server provides such elements as a heartbeat, common
JavaScript, policies, handlers, such as an observer and a guide,
and the affinity engine. In FIG. 30, the coordinator is a piece of
code that sits on the trusted Web site. In the presently preferred
embodiment, the coordinator resides on a customer's Web site. Thus,
there is a file that serves as coordinator role and that resides on
a particular customer Web site, although it could reside on any
other trusted Web site. This aspect of the invention concerns the
notion of a trusted Web server for failsafe reasons. It is
important to be certain that if the invented system crashes that it
does not crash or hold up the customer Web site. Thus, a
coordinator code is provided as a failsafe procedure. It goes
through a failsafe procedure to ensure proper recovery after a
crash of the server's Web site.
[0150] The architecture of the code is broken down, such that
common code is used. Common code or a piece of the code is used for
all types of recommendations. Thus, a tag might require common
code. The policy may be customer specific or it can be user
specific. This concerns how recommendations may be made. The policy
information is loaded separately. Then, the code needed for
specific tags is loaded as well. For example, if there is a tag for
the most popular recommendation, and another one which is the
recommendation, then these tags are loaded. The system then loads
the code that is necessary for performing the particular tagging
operations. A further type of tag or code is the observer that
observes what users do, as discussed above. Users do not see the
tag, but it is still a tag. The tags must also be able to
communicate back to the system. Each of these functions is
performed by a piece of code, for example JavaScript, is loaded
into the Web page. The JavaScript is loaded into the page through a
dynamic script injection.
[0151] This aspect of the invention recognizes that a Web page can
become more dynamic. For example, there are things such as dynamic
HTML and JavaScript which cause things to be added to a page
dynamically. If a script tag needs a particular piece of
information, then that code is injected into the page. For example,
if a policy is needed, then a JavaScript tag is injected into the
page that accesses the policy. The policy may require further
injection of information, such as handlers. Therefore, the handlers
are now dynamically injected as pieces of JavaScript into the page.
Communications are handled in a similar matter.
[0152] One advantage of the foregoing approach is to provide the
option of using first-party cookies or third-party cookies. The
main Web page that the customer accesses may be the Web site of a
merchant. The merchant communicates with the system to get the
information with regard to recommendations and the like.
[0153] In this case, the merchant is the first party and the system
is a third party. As a result of dynamic script injection, the
invention allows the setting of cookies in both the first-party
domain and in the third-party domain. The ability to set first and
third-party cookies also allows the invention to operate in a cross
domain.
[0154] Although the invention is described herein with reference to
the preferred embodiment, one skilled in the art will readily
appreciate that other applications may be substituted for those set
forth herein without departing from the spirit and scope of the
present invention. Accordingly, the invention should only be
limited by the Claims included below.
* * * * *