U.S. patent application number 13/689512 was filed with the patent office on 2013-06-06 for identifying and exposing item purchase tendencies of users that browse particular items.
This patent application is currently assigned to AMAZON TECHNOLOGIES, INC.. The applicant listed for this patent is Amazon Technologies, Inc.. Invention is credited to Randal M. Henne, Jeffrey Alan Holden, David Hsu, Dennis Lee, Brent R. Smith, Eric R. Vadon.
Application Number | 20130144684 13/689512 |
Document ID | / |
Family ID | 47227361 |
Filed Date | 2013-06-06 |
United States Patent
Application |
20130144684 |
Kind Code |
A1 |
Lee; Dennis ; et
al. |
June 6, 2013 |
IDENTIFYING AND EXPOSING ITEM PURCHASE TENDENCIES OF USERS THAT
BROWSE PARTICULAR ITEMS
Abstract
A data mining system generates data values reflecting purchase
tendencies of users who browse particular items in an electronic
catalog. The data values may include conditional probability values
representing, for example, a probability that, if a user makes a
purchase after browsing a first particular item, the user will
purchase a second particular item. The data values may be used to
provide notifications on product pages of an electronic catalog.
For example, an item detail page for a first item may be
supplemented with a notification of one or more other items that
tend to be purchased by those who browse the first item.
Inventors: |
Lee; Dennis; (Mercer Island,
WA) ; Henne; Randal M.; (Seattle, WA) ;
Holden; Jeffrey Alan; (Seattle, WA) ; Hsu; David;
(Seattle, WA) ; Smith; Brent R.; (Redmond, WA)
; Vadon; Eric R.; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc.; |
Seattle |
WA |
US |
|
|
Assignee: |
AMAZON TECHNOLOGIES, INC.
Seattle
WA
|
Family ID: |
47227361 |
Appl. No.: |
13/689512 |
Filed: |
November 29, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10864288 |
Jun 9, 2004 |
8326658 |
|
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13689512 |
|
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|
60561776 |
Apr 12, 2004 |
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Current U.S.
Class: |
705/7.29 |
Current CPC
Class: |
G06Q 30/0201 20130101;
G06Q 30/0603 20130101 |
Class at
Publication: |
705/7.29 |
International
Class: |
G06Q 30/02 20120101
G06Q030/02 |
Claims
1. A data mining method, comprising: recording item browsing events
and item purchase events of each of a plurality of users of an
electronic catalog of items, each item browsing event representing
a user access to a description of a catalog item in the electronic
catalog, each item purchase event representing a purchase by a user
of a catalog item; for an item pair consisting of a first catalog
item and a second catalog item, generating a conditional
probability value based on the recorded item browsing events and
item purchase events, said conditional probability value at least
approximately representing a probability that, if a user makes a
purchase after browsing the first catalog item, the user will
purchase the second catalog item, said conditional probability
value generated programmatically by a computer; and generating a
notification for presentation on an electronic catalog page
corresponding to the first catalog item, said notification
identifying the second item, and being based at least partly on the
conditional probability value.
2. The method of claim 1, wherein generating the conditional
probability value comprises calculating a ratio of (a) a number of
users who purchased the second catalog item after browsing the
first catalog item, to (b) a number of users who made a purchase of
a catalog item after browsing the first catalog item.
3. The method of claim 2, wherein calculating said ratio comprises
excluding from consideration at least one of the following types of
purchases: (1) purchases of items falling outside an item category
of the first catalog item; (2) purchases that occur outside a
browsing session in which the first catalog item is viewed; (3)
purchases that occur after an intervening purchase.
4. The method of claim 1, wherein generating the conditional
probability value comprises calculating a ratio of (a) a number of
users who both browsed the first catalog item and purchased the
second catalog item, to (b) a number of users who both browsed the
first catalog item and made a purchase of any of a plurality of
catalog items.
5. The method of claim 1, wherein the notification includes a
representation of the conditional probability value.
6. A computing system, comprising: a computer data repository that
stores item browsing event data and item purchase event data, said
item browsing event data reflecting item browsing events of users
of an electronic catalog, said item purchase event data reflecting
item purchases of users of the electronic catalog; and a computer
system comprising computer hardware, said computer system
programmed to use the item browsing event data and item purchase
event data in combination to generate conditional probability
values for respective pairs of catalog items, each pair consisting
of a first catalog item and a second catalog item, each conditional
probability value at least approximately representing a probability
that, if a user who browses the first catalog item makes a
purchase, the user will purchase the second catalog item.
7. The system of claim 6, wherein the computer system is programmed
to generate the conditional probability values by a process that
comprises calculating a ratio of (a) a number of users who
purchased the second catalog item after browsing the first catalog
item, to (b) a number of users who made a purchase of a catalog
item after browsing the first catalog item.
8. The system of claim 6, wherein the computer system is programmed
to generate the conditional probability values by a process that
comprises calculating a ratio of (a) a number of users who both
browsed the first catalog item and purchased the second catalog
item, to (b) a number of users who both browsed the first catalog
item and made a purchase of a catalog item.
9. The system of claim 6, wherein the computer system is programmed
to use the conditional probability values to generate notifications
for display on catalog pages of the electronic catalog.
10. The system of claim 6, wherein the computer system is
configured to generate, based at least partly on the conditional
probability values, a notification that a particular second catalog
item is purchased by users who browse a particular first catalog
item; and output the notification for display on a catalog page
associated with the particular first catalog item.
11. The system of claim 11, wherein the notification includes a
representation of a corresponding conditional probability
value.
12. The system of claim 6, wherein the computer system is
configured to use the conditional probability values to select
particular second catalog items to list on catalog pages
corresponding to particular first catalog items.
13. A computer system, comprising: a computer data repository that
stores conditional probability values for respective pairs of
catalog items, each of said conditional probability values at least
approximately representing a probability that, if a user who
browses a first particular catalog item makes a purchase, the user
will purchase a second particular catalog item, said conditional
probability values based on recorded item browsing events and item
purchase events of a plurality of users; and a server that uses the
conditional probability values to supplement pages of an electronic
catalog with notifications regarding purchase tendencies of users
who browse particular catalog items, said server comprising
computer hardware.
14. The computer system of claim 13, wherein the notifications
include representations of the conditional probability values.
15. The computer system of claim 13, wherein the computer data
repository additionally stores conditional probability values for
individual items, each conditional probability value for an
individual item at least approximately representing a probability
that, if a user who views a particular item user makes a purchase,
the user will purchase the particular item, wherein the server
additionally uses the conditional probability values for individual
items to supplement the pages of the electronic catalog with said
notifications.
16. The computer system of claim 13, wherein the conditional
probability values are based solely on purchases made after
corresponding item browsing events.
17. A computer system comprising computer hardware, said computer
system configured to implement a process that comprises:
generating, based on recorded item browsing events and item
purchase events of a plurality of users, a probability value for a
pair of catalog items consisting of a first catalog item and a
second catalog item, said probability value representing a
probability that, if a user who browses the first catalog item
makes a purchase, the user will purchase the second catalog item;
and providing a representation of the probability value on an
electronic catalog page corresponding to the first catalog item to
assist users in making item selection decisions.
18. The computer system of claim 17, wherein generating the
probability value comprises calculating a ratio of (a) a number of
users who both browsed the first catalog item and purchased the
second catalog item, to (b) a number of users who both browsed the
first catalog item and made a purchase of a catalog item.
19. The computer system of claim 17, wherein the computer system is
additionally configured to: generate a second probability value
representing a likelihood that, if the user who browses the first
catalog item makes a purchase, the user will purchase the first
catalog item; and provide a representation of the second
probability value on the electronic catalog page.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 10/864,288, filed Jun. 9, 2004, which claims the benefit of
U.S. Provisional Application No. 60/561,776, filed Apr. 12,
2004.
FIELD OF THE INVENTION
[0002] The present invention relates to data mining processes for
analyzing browse histories of users of an electronic catalog.
BACKGROUND OF THE INVENTION
[0003] A variety of technologies exist for collecting and mining
user activity data reflective of the actions and preferences of
users of an electronic catalog. For example, it is known in the art
to collectively analyze the activity data of a population of users
to identify items that tend to be viewed, purchased, or otherwise
selected in combination. Different types of item relationships may
be detected by applying different similarity metrics to the
activity data. For instance, a pair of items, A and B, may be
identified as likely substitutes because a relatively large number
of the users who viewed A also viewed B during the same browsing
session. Items C and D, on the other hand, may be identified as
complementary because a relatively large number of those who
purchased C also purchased D.
[0004] The item relationships detected through this process may be
exposed to users of the electronic catalog to assist users in
identifying items of interest. For example, in some systems, when a
user views a catalog item, the user is informed of other items that
are commonly viewed (or purchased) by those who have viewed (or
purchased) this item. Although this type of data assists the user
in identifying a set of candidate items from which to make a
selection (e.g., a set of consumer electronics products with
similar features), it generally does not help the user discriminate
between these candidate items. Thus, the user typically must rely
solely on the descriptions of the candidate items, including any
user ratings and reviews, in making a purchase decision.
SUMMARY
[0005] The present invention comprises data mining methods for
analyzing user activity data associated with an electronic catalog
of items to generate data for assisting users in making informed
item selection decisions. The invention may be embodied within any
type of electronic catalog system (web site, online services
network, multi-site "mall" system, etc.) in which users can select
catalog items to purchase, rent, download, or otherwise
acquire.
[0006] In one embodiment, a data mining component analyzes the user
activity data of a population of users, as collected over a period
of time, to generate conditional probability values for specific
pairs of items in the electronic catalog. Each conditional
probability value preferably indicates, for a given item pair (item
A, item B), what percentage of the users who browsed item A and
thereafter acquired an item acquired item B. For example, in the
context of purchases, the conditional probability value may
indicate what percentage of the users who made a purchase after
viewing item A purchased item B. Certain types of purchases, such
as purchases of items outside the item category of item A and/or
purchases made beyond a selected time interval after viewing item
A, may be disregarded for purposes of this calculation.
[0007] The conditional probability values may be displayed in the
catalog in association with specific items to assist users in
making informed item selection decisions. For instance, in one
embodiment, when a user accesses an item detail page, the user is
presented with a list of the following general form: "users who
purchased an item after viewing this item bought the following: 4%
bought item A, 2% bought this item, and 1.5% bought item B." Thus,
in addition to being informed of other items that are similar or
related to the item selected for viewing, the user is provided with
data that assists the user in selecting between the various related
items. This data allows the user to very efficiently rely on the
research conducted, and the ultimate purchase decisions made, by
prior users who considered this particular item.
[0008] In another embodiment, the user activity data is analyzed to
generate conditional probability data for specific sets of items
that may be viewed during the course of a browsing session (e.g.,
the set consisting of items A and B). Once a user views all of the
items in such a set, the associated conditional probability data
may be presented to the user. For example, if a user selects both
item A and item B for viewing during a browsing session, the user
may be notified that "users who made a purchase after viewing item
A and item B bought the following: 5% bought item B, 2% bought item
C, and 1% bought item A."
[0009] Condition probability data may also be generated and
presented in other contexts. For example, in one embodiment, a
browse node page corresponding to a category of products is
augmented with a notification that "user's who made a purchase
after accessing this page bought the following items . . . ,"
together with associated percentage values. In another embodiment,
a user who conducts a search for <search query> is notified
that "users who made a purchase after searching for <search
query> purchased the following items . . . ," together with
associated percentage values.
[0010] Thus, one aspect of the invention is a data mining method
that comprises programmatically analyzing user activity data to
identify a set of users that acquired an item from an electronic
catalog after performing a defined set of one or more browsing
actions. The defined set of browsing actions may, for example, be
the viewing of a particular item or set of items, the viewing of a
particular web page, or the submission of a particular search
query. The method further comprises calculating, for at least a
first item in the electronic catalog, a percentage of the set of
users acquired the first item after performing the defined set of
browsing actions. When a user performs the defined set of browsing
actions, the user is notified of said percentage of users that
selected the first item.
[0011] Neither this summary nor the following detailed description
purports to define the invention. The invention is defined by the
claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 illustrates a web site system according to one
embodiment of the invention.
[0013] FIG. 2 illustrates a product detail page generated in
accordance with one embodiment of the invention.
[0014] FIG. 3 illustrates one example of a data mining method that
may be used to generate a mapping table of the type shown in FIG.
1.
[0015] FIG. 4 illustrates an example search results page in
accordance with one embodiment of the invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0016] Specific embodiments of the invention will now be described
with reference to the drawings. These embodiments are intended to
illustrate, and not limit, the present invention. For example,
although the specific embodiments described herein involve the
generation and display of data regarding item purchase events, the
invention is also applicable to other types of item acquisition
actions, including rentals, licenses and downloads of items.
[0017] FIG. 1 illustrates a web site system 30 according to one
embodiment of the invention. The web site system 30 includes a web
server 32 that generates and serves pages of a host web site to
computing devices 35 of end users. The web server 32 provides user
access to a catalog of items represented within a database 36. The
items preferably include or consist of items that may be purchased
via the web site (e.g., consumer electronics products; household
appliances; book, music and video titles in physical or
downloadable form; magazine and other subscriptions, etc.), and may
be arranged within a hierarchy of browse categories. Many thousands
or millions of different items may be represented in the database
36. The web site system 30 may, for example, include all of the
components and features described in U.S. Patent Pub. No. US
2002/0019763 A1, published Feb. 14, 2002 ("the '763 publication"),
the disclosure of which is hereby incorporated by reference.
[0018] Detailed information about each item may be obtained by
users by accessing the item's detail page within the catalog, which
may be located, for example, by conducting a search for the item or
by selecting the item from a browse tree listing. Each item detail
page preferably provides an option for the user to purchase the
item. The web server 32 preferably generates the item detail pages,
and other pages of the web site, dynamically using web page
templates (not shown).
[0019] As illustrated in FIG. 1, the web site system 30 maintains
time-stamped item browse histories 38 and item purchase histories
40 of users of the system. The item browse history 38 of a user
identifies the items selected by that user for viewing, and
indicates the date and time of each such item browse event. In the
preferred embodiment, each user access to an item detail page is
treated as an item viewing or browsing event. Typically, each
access to an item detail page occurs as the result of an
affirmative selection of the corresponding item for viewing (e.g.,
from a search results page or a browse node page). The purchase
history 40 of each user identifies all of the items purchased by
that user, together with the associated dates of purchase. The item
browse and purchase histories 38, 40 of the users may be maintained
in any suitable format (web log, user database, event history
database, etc.), and may but need not be stored separately from
each other and from other types of user activity data.
[0020] In some embodiments, the browse and/or purchase histories
38, 40 may include or consist of data obtained from external
sources. For example, user activity data may additionally be
acquired from third party web site operators, and/or from browser
toolbars that report users' browsing activities.
[0021] As further illustrated in FIG. 1, a table generation process
44 periodically and collectively analyzes or "mines" the item
browse and purchase histories of the users to generate a
browse-purchase mapping table 46. Each entry (depicted as a row) of
this table 46 maps a reference catalog item to a listing of the
catalog items that have been purchased the most frequently, within
a selected time period (e.g., one day or one week), by users who
have viewed the reference catalog item. A separate table entry may
be included for each item in the catalog for which the collected
browse and purchase activity data is sufficient to generate
meaningful data. As depicted by the example table data in FIG. 1,
the list of purchased items for a given reference item may include
the reference item itself. The table generation process 44 may be
in the form of a program that runs on a general purpose computer,
and may be executed periodically in an off-line processing
mode.
[0022] Each "purchased item" within a given table entry is stored
together with a conditional probability value 50 that indicates or
reflects the relative frequency with which users who viewed the
reference item thereafter (within a selected time period) bought
the purchased item. In a preferred embodiment, the conditional
probability value 50 for a given browsed item/purchased item pair
is calculated by taking the ratio of (a) the number of users who
bought the purchased item within the selected time period after
viewing the browsed item, and (b) the total number of users who
purchased any item within the selected time period after viewing
the browsed item. Certain types of purchases may optionally be
disregarded for purposes of this calculation, such as some or all
of the following: (a) purchases of items falling outside an item
category, such as the top-level browse category, of the browsed
item; (b) purchases that occur outside a browsing session in which
the browsed item is viewed; (c) purchases that occur only after the
relevant user had made one or more intervening purchases after
viewing the browsed item. Any of a variety of alternative
calculations may be used to generate the conditional probability
values.
[0023] As depicted in FIG. 1, the web server 32 accesses the
browse-purchase mapping table 46 in order to supplement item detail
pages of the catalog. Specifically, upon receiving a request for
the item detail page for a particular catalog item, the web server
32 retrieves, and incorporates into the detail page, all or a
portion of the corresponding listing of purchased items, including
the associated conditional probability values. This listing may
alternatively be retrieved from a cache, or may be included within
the static or semi-static content of the item detail page.
[0024] FIG. 2 illustrates one example of how this data may be
presented on an item detail page. In this example, the user is
viewing a web page associated with a particular item (product)
falling within the "kitchen and housewares" category of the
catalog. The page has been supplemented with a list 60 of the four
"kitchen and housewares" items that have been purchased the most
frequently (within the selected time period) by users who have
viewed the item detail page for this product. The associated
conditional probability values 50 are displayed as percentage
values. The list 60 of items and associated values 50 is referred
to herein as a conditional probability list. ("Conditional
probability" refers generally to the probability of an event
occurring given that another event, or set of events, has
occurred.)
[0025] In this particular example, the product that is featured on
the item detail page is also included (in bold) in the conditional
probability list 60. The other products included in the list 60 are
preferably products that are similar or related to the product
featured on the detail page, such as products that fall within the
same bottom-level product/browse category as the featured item.
Thus, in addition to informing the user of possible alternatives to
the product selected for viewing, the item detail page provides the
user with data regarding the ultimate purchase decisions made by
others who have viewed the same item. This data allows the user to
very efficiently benefit from the product research conducted by
prior users who have viewed this particular product.
[0026] Although the conditional probability list 60 is displayed on
an item detail page in this example, the list 60 may alternatively
be presented to the user elsewhere, such as in a pop-up window or
in a browser toolbar display area. In addition, although the
conditional probability values 50 are displayed numerically in this
example, they may alternatively be presented graphically (e.g.,
within a bar chart or pie chart, via color coding or shading, using
icons, etc.).
[0027] As will be apparent, conditional probability data of the
type shown in FIG. 2 may also be generated, and presented
contextually, for other types of pages of the electronic catalog.
For example, the browse node (category) page for a given item
category may be augmented with a conditional probability list 60 of
the following form: "users who made a purchase in this item
category after viewing this page bought . . . ." The process for
generating such a list 60 is substantially the same as described
herein, but incorporates activity data regarding visits to browse
node pages in place of data regarding visits to item detail
pages.
[0028] FIG. 3 illustrates an example process (sequence of steps)
that may be performed by the table generation process 44 of FIG. 1
to generate the mapping table 46. This process may be repeated
periodically (e.g., once a week) to update or regenerate the table
46 so that the table data reflects the most recent set of user
activity data. Typically, the table 46 will be generated based on
the browse and purchase actions of many thousands or hundreds of
thousands of users. For purposes of generating the table and
displaying conditional probability data, different versions of a
given product or work may be treated as the same item. Examples
include hardcover and paperback versions of the same book title,
video and DVD versions of the same movie title, CD and tape
versions of the same music title, and different releases or
editions of a particular product.
[0029] In step 66, the browse histories of all users of the system
are retrieved, and are used to build a temporary table that maps
user/date information to browsed items. This table indicates, for
each user and each date, what items were viewed by that user on
that date. In a preferred embodiment in which each item has a
corresponding detail page in the catalog, a user is treated as
having viewed or browsed an item if and only if the user accessed
the item's detail page. The invention does not, however, require
the use or existence of item detail pages.
[0030] In step 68, the purchase histories of all users are
retrieved, and are similarly used to generate a temporary table
that maps user/date information to purchased items. As mentioned
above, histories of other types of acquisition events (item
downloads, rental selections, etc.) may additionally or
alternatively be used to build this table. The two temporary tables
may, but need not, be limited to a particular date range (e.g., the
last three months).
[0031] In step 70, the temporary tables are used to count, for each
browsed item/purchased item pair, how many users bought the
purchased item within 0-N days after viewing the browsed item. (To
simplify the analysis, a purchase of the purchased item is
preferably assumed to have occurred after the viewing of the
browsed item if both events occurred on the same day.) The value N
defines the selected time interval for purposes of calculating the
conditional probability values, and may, for example, be a value
such as 1, 10 or infinity. Rather than defining the selected time
period in terms of days, the time period may, for example, be
defined in terms of hours, minutes. Although not depicted in FIG.
3, the process may exclude from consideration (a) purchases made
outside a browsing session in which the browsed item was viewed,
and/or (b) purchases that followed an intervening purchase, as
mentioned above.
[0032] In step 72, the count value generated for each browsed
item/purchased item pair is divided by the total number of users
that purchased any item within 0-N days after viewing the browsed
item. The result of this calculation is the corresponding
conditional probability value. Thus, for example, assuming that one
thousand users made a purchase 0-N days after viewing item A, and
that one hundred of those users purchased item B 0-N days after
viewing item A, the conditional probability value 50 for the pair
item A/item B would be 100/1000, or 10%, in the embodiment depicted
in FIG. 3. As mentioned above, purchases of items falling outside
the top-level category of the browsed item may optionally be
disregarded for purposes of this calculation.
[0033] In step 74, item pairs that are not included within a
similar items table 64 (FIG. 1) are optionally filtered out of the
results set. The effect of this step is to inhibit or prevent the
conditional probability listings 60 from including items that are
not closely related to the item featured on the detail page. In a
preferred embodiment, the similar items table 64 is generated using
the process depicted in FIG. 3B of the '763 publication.
Specifically, the similar items table 64 identifies pairs of items
that have co-occurred relatively frequently within the
session-specific browsing histories of users (e.g., items A and B
are related because a relatively large number of the users who
viewed item A also viewed item B during the same browsing session).
One attribute of this relatedness metric is that the items
identified as being related are typically substitutes for each
other. Any of a variety of alternative item similarity metrics may
be used. In addition, rather than using a similar items table 64,
the filtering in step 74 may be performed by retaining only those
pairs for which both items fall within the same bottom-level or
other item category.
[0034] In step 76, the remaining set of "purchased items" for a
given "browsed item" are sorted from highest to lowest conditional
probability value, and the top M "purchased items" are retained for
inclusion within the mapping table 46. Thus, M item pairs are
effectively retained, each of which is composed of the browsed item
and one of the retained purchased items. M may be a selected
integer such as five or ten. Thus, for example, although many
different items may have been purchased by users who viewed item A,
only those M items that were purchased relatively frequently by
users who viewed item A are included within the table entry for
item A. In step 0078, the remaining (retained) item pairs and
corresponding conditional probability values are stored in the
mapping table 46 (FIG. 1). Because the process is fully automated,
table entries may be generated, and conditional probability lists
60 may be displayed, for many hundreds of thousands or millions of
different catalog items.
[0035] The above-described processes may also be used to provide
conditional probability data to users who conduct keyword searches.
For example, as depicted in FIG. 4, when a user runs a search for
"mp3 player," the search results page may be supplemented with a
conditional probability list 60 indicating the frequencies with
which users that have purchased a product after searching for "mp3
player" have purchased specific products. To implement this
variation, the table generation process 44 may be modified to
perform the following steps: [0036] analyze user activity data
reflective of user search activities to identify the most
frequently submitted search queries (e.g., the 100,000 most common
search queries); [0037] for each such search query, identify a set
of users that submitted the search query and then made a purchase,
and [0038] for each such set of users, calculate, for each of a
plurality of items purchased by one or more users in the set after
submitting the respective search query, what percentage of the set
of users purchased the respective purchased item.
[0039] Some or all of the resulting percentages (conditional
probability values) may be stored in a mapping table that maps
search queries to corresponding lists 60 of purchased items. When a
user submits a search query, the list 60 of purchased items
associated with that search query, including the associated
percentage values 50, may be retrieved from the mapping table and
incorporated into the search results page returned to the user.
Conditional probability lists 60 may be omitted from search results
pages where none of the conditional probability values associated
with the query exceeds a particular threshold, such as 1%.
[0040] In another embodiment of the invention, the user activity
data is analyzed to generate conditional probability data for
specific sets of two or more viewed items. This data may be
presented to users that view all of the items in the set during the
current browsing session. For example, once a user has selected
item A and item B for viewing, the user may be notified that "users
who made a purchase after viewing item A and item B bought the
following: 5% bought item B, 2% bought item C, and 1% bought item
A." To implement this feature, the browse histories of users may be
analyzed to identify specific sets of N items that are frequently
viewed during the same browsing session, where N may be two, three
or four, for example. Once such a set of N items is identified, the
users who viewed all N items and then made a purchase are
identified. The percentages of these users that bought specific
items are then calculated and stored in a look-up table in
association with the set of items. As a user browses the catalog,
the user's session record (set of viewed items) may be compared to
this look-up table to determine whether a match exists.
[0041] In yet another embodiment, conditional probability data is
generated that reflects the frequencies with which users who
purchase a first item also purchase each of a plurality of second
items. This data may be displayed on item detail pages in the same
manner as described above. For example, the detail page for a
particular item may be augmented to display a message of the
following form: "users who purchased this item also purchased the
following: X% bought item A, Y% bought item B . . . ." The
conditional probability data may optionally be calculated so as to
take into consideration the order in which the items were
purchased. For instance, to generate data to display on the detail
page for item A, the conditional probability calculations may be
based solely on purchases made after a purchase of item A. The
messaging on item A's detail page may then read generally as
follows: "User's who made a purchase after buying this item bought
the following . . . ."
[0042] As mentioned above, the invention is not limited to purchase
events, but rather applies to a variety of different types of item
acquisition actions. For example, for purposes of the generating
conditional probability data, an item may be treated as having been
"acquired" by a user if the user has placed an order to rent or
purchase the item or, in the case of downloadable items, has
initiated a download of the item. In some embodiments, the act of
adding an item to an electronic shopping cart or wish list may also
be treated as an acquisition of the item.
[0043] The invention may also be applied more generally to a wide
range of other types of browsing actions and events. Thus, in
general, whenever a user performs a particular set of one or more
conditioning actions (page views, item views, item purchases,
browse node accesses, search query submissions, etc.), the user may
be provided with conditional probability data reflective of the
frequencies with which users who have performed this set of
conditioning events have also (or have thereafter) performed
specific predictive events. Each such predictive action may, but
need not, correspond to a particular item in an electronic catalog,
such that the user is provided with guidance for selecting items
from the catalog. Thus, the invention includes a data mining method
that comprises programmatically analyzing user activity data to
calculate a frequency with which users who have performed a defined
predictive action after performing a defined set of one or more
conditioning actions have performed the predictive action in
association with a first item; when a user performs the set of one
or more conditioning actions, the user is notified of said
frequency to assist the user in evaluating the first item as a
candidate.
[0044] As will be apparent, the features and attributes of the
specific embodiments disclosed above may be combined in different
ways to form additional embodiments, all of which fall within the
scope of the present disclosure.
[0045] Although this invention has been described in terms of
certain preferred embodiments and applications, other embodiments
and applications that are apparent to those of ordinary skill in
the art, including embodiments which do not provide all of the
features and advantages set forth herein, are also within the scope
of this invention. Accordingly, the scope of the present invention
is intended to be defined only by reference to the appended claims,
which are intended to be construed without reference to the
incorporated-by-reference materials.
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