Patent Abstract:
A computer-implemented recommendation service uses item-to-item relationship mappings to select items to recommend to the user. The item-to-item relationship mappings may reflect user-behavior-based (e.g., purchase-based) item relationships, content-based item relationships, or a combination thereof. In one embodiment, personalized recommendations are generated for a user by a process that comprises retrieving from the mapping, for each of a plurality of items of interest to the user, a respective related items list; weighting the related items lists based on information regarding the user&#39;s affinity for the corresponding items of interest; combining the weighted related items lists to form a pool of scored items, and selecting items from the pool to recommend to the user.

Full Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 11/841,971, filed Aug. 20, 2007 now U.S. Pat. No. 8,024,222, which is a continuation of application Ser. No. 11/394,999, filed Mar. 31, 2006 (now U.S. Pat. No. 7,908,183), which is a continuation of U.S. patent application Ser. No. 09/850,263, filed May 7, 2001 (now U.S. Pat. No. 7,113,917), which is a continuation of U.S. application Ser. No. 09/156,237, filed Sep. 18, 1998 (now U.S. Pat. No. 6,317,722), the technical disclosure of which is hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to information filtering and recommendation systems. More specifically, the disclosure relates to methods for predicting the interests of individual users based on the known interests of a community of users. 
     BACKGROUND 
     A recommendation service is a computer-implemented service that recommends items from a database of items. The recommendations are customized to particular users based on information known about the users. One common application for recommendation services involves recommending products to online customers. For example, online merchants commonly provide services for recommending products (books, compact discs, videos, etc.) to customers based on profiles that have been developed for such customers. Recommendation services are also common for recommending Web sites, articles, and other types of informational content to users. 
     One technique commonly used by recommendation services is known as content-based filtering. Pure content-based systems operate by attempting to identify items which, based on an analysis of item content, are similar to items that are known to be of interest to the user. For example, a content-based Web site recommendation service may operate by parsing the user&#39;s favorite Web pages to generate a profile of commonly-occurring terms, and then use this profile to search for other Web pages that include some or all of these terms. 
     Content-based systems have several significant limitations. For example, content-based methods generally do not provide any mechanism for evaluating the quality or popularity of an item. In addition, content-based methods generally require that the items include some form of content that is amenable to feature extraction algorithms; as a result, content-based systems tend to be poorly suited for recommending movies, music titles, authors, restaurants, and other types of items that have little or no useful, parsable content. 
     Another common recommendation technique is known as collaborative filtering. In a pure collaborative system, items are recommended to users based on the interests of a community of users, without any analysis of item content. Collaborative systems commonly operate by having the users rate individual items from a list of popular items. Through this process, each user builds a personal profile of ratings data. To generate recommendations for a particular user, the user&#39;s profile is initially compared to the profiles of other users to identify one or more “similar users.” Items that were rated highly by these similar users (but which have not yet been rated by the user) are then recommended to the user. An important benefit of collaborative filtering is that it overcomes the above-noted deficiencies of content-based filtering. 
     As with content-based filtering methods, however, existing collaborative filtering techniques have several problems. One problem is that the user is commonly faced with the onerous task of having to rate items in the database to build up a personal ratings profile. This task can be frustrating, particularly if the user is not familiar with many of the items that are presented for rating purposes. Further, because collaborative filtering relies on the existence of other, similar users, collaborative systems tend to be poorly suited for providing recommendations to users that have unusual tastes. 
     Another problem with collaborative filtering techniques is that an item in the database normally cannot be recommended until the item has been rated. As a result, the operator of a new collaborative recommendation system is commonly faced with a “cold start” problem in which the service cannot be brought online in a useful form until a threshold quantity of ratings data has been collected. In addition, even after the service has been brought online, it may take months or years before a significant quantity of the database items can be recommended. 
     Another problem with collaborative filtering methods is that the task of comparing user profiles tends to be time consuming—particularly if the number of users is large (e.g., tens or hundreds of thousands). As a result, a tradeoff tends to exist between response time and breadth of analysis. For example, in a recommendation system that generates real-time recommendations in response to requests from users, it may not be feasible to compare the user&#39;s ratings profile to those of all other users. A relatively shallow analysis of the available data (leading to poor recommendations) may therefore be performed. 
     Another problem with both collaborative and content-based systems is that they generally do not reflect the current preferences of the community of users. In the context of a system that recommends products to customers, for example, there is typically no mechanism for favoring items that are currently “hot sellers.” In addition, existing systems do not provide a mechanism for recognizing that the user may be searching for a particular type or category of item. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure addresses these and other problems by providing a computer-implemented service and associated methods for generating personalized recommendations of items. An important benefit of the service is that the recommendations are generated without the need for the user, or any other users, to rate items. Another important benefit is that the recommended items are identified using a previously-generated table or other mapping structure which maps individual items to lists of related or “similar” items. The item similarities reflected by the table are preferably based at least partly upon correlations between the interests of users in particular items, but may additionally or alternatively be based on content-based item relationships. 
     The types of items that can be recommended by the service include, without limitation, books, compact discs (“CDs”), videos, authors, artists, item categories, Web sites, and chat groups. The service may be implemented, for example, as part of a Web site, online services network, e-mail notification service, document filtering system, or other type of computer system that explicitly or implicitly recommends items to users. In a preferred embodiment described herein, the service is used to recommend works such as book titles and music titles to users of an online merchant&#39;s Web site. 
     The mappings of items to similar items (“item-to-item mappings”) may be generated periodically, such as once per week, by an off-line process which identifies correlations between known interests of users in particular items. For example, in the embodiment described in detail below, the mappings are generating by periodically analyzing user purchase histories to identify correlations between purchases of items. The similarity between two items is preferably measured by determining the number of users that have an interest in both items relative to the number of users that have an interest in either item (e.g., items A and B are highly similar because a relatively large portion of the users that bought one of the items also bought the other item). The item-to-item mappings could also incorporate other types of similarities, including content-based similarities extracted by analyzing item descriptions or content. 
     To generate a set of recommendations for a given user, the service retrieves from the table the similar items lists corresponding to items already known to be of interest to the user, and then appropriately combines these lists to generate a list of recommended items. For example, if there are three items that are known to be of interest to the user (such as three items the user recently purchased), the service may retrieve the similar items lists for these three items from the table and combine these lists. 
     The similar items lists read from the table may be appropriately weighted (prior to being combined) based on indicia of the user&#39;s affinity for, or current interest in, the corresponding items of known interest. For example, the similar items list for a book that was purchased in the last week may be weighted more heavily than the similar items list for a book that was purchased four months ago. Weighting a similar items list heavily has the effect of increasing the likelihood that the items in that list will be included in the recommendations that are ultimately presented to the user. 
     An important aspect of the service is that the relatively computation-intensive task of correlating item interests is performed off-line, and the results of this task (item-to-item mappings) stored in a mapping structure for subsequent look-up. This enables the personal recommendations to be generated rapidly and efficiently (such as in real-time in response to a request by the user), without sacrificing breadth of analysis. 
     Another feature involves using the current and/or recent contents of the user&#39;s shopping cart as inputs to the recommendation service (or to another type of recommendation service which generates recommendations given a unary listing of items). For example, if the user currently has three items in his or her shopping cart, these three items can be treated as the items of known interest for purposes of generating recommendations, in which case the recommendations may be generated and displayed automatically when the user views the shopping cart contents. Using the current and/or recent shopping cart contents as inputs tends to produce recommendations that are highly correlated to the current short-term interests of the user—even if these short term interest differ significantly from the user&#39;s general preferences. For example, if the user is currently searching for books on a particular topic and has added several such books to the shopping cart, this method will more likely produce other books that involve the same or similar topics. 
     Another feature involves allowing the user to create multiple shopping carts under a single account (such as shopping carts for different family members), and generating recommendations that are specific to a particular shopping cart. For example, the user can be prompted to select a particular shopping cart (or set of shopping carts), and the recommendations can then be generated based on the items that were purchased from or otherwise placed into the designated shopping cart(s). This feature allows users to obtain recommendations that correspond to the role or purpose (e.g., work versus pleasure) of a particular shopping cart. 
     Two specific implementations of the service are disclosed, both of which generate personal recommendations using the same type of table. In the first implementation, the recommendations are based on the items that have recently been rated or purchased by the user. In the second implementation, the recommendations are based on the current shopping cart contents of the user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features will now be described with reference to the drawings summarized below. These drawings and the associated description are provided to illustrate a preferred embodiment of the invention, and not to limit the scope of the invention. 
         FIG. 1  illustrates a Web site which implements a recommendation service which operates in accordance with the invention, and illustrates the flow of information between components. 
         FIG. 2  illustrates a sequence of steps that are performed by the recommendation process of  FIG. 1  to generate personalized recommendations. 
         FIG. 3  illustrates a sequence of steps that are performed by the table generation process of  FIG. 1  to generate a similar items table, and illustrates temporary data structures generated during the process. 
         FIG. 4  is a Venn diagram illustrating a hypothetical purchase history profile of three items. 
         FIG. 5  illustrates one specific implementation of the sequence of steps of  FIG. 2 . 
         FIG. 6  illustrates the general form of a Web pages used to present the recommendations of the  FIG. 5  process to the user. 
         FIG. 7  illustrates another specific implementation of the sequence of steps of  FIG. 2 . 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     The various features and methods of the invention will now be described in the context of a recommendation service, including two specific implementations thereof, that is used to recommend book titles, music titles, video titles, and other types of items to individual users of the Amazon.com Web site. As will be recognized to those skilled in the art, the disclosed methods can also be used to recommend other types of items, including non-physical items. By way of example and not limitation, the disclosed methods can also be used to recommend authors, artists, categories or groups of titles, Web sites, chat groups, movies, television shows, downloadable content, restaurants, and other users. 
     Throughout the description, reference will be made to various implementation-specific details of the recommendation service, the Amazon.com Web site, and other recommendation services of the Web site. These details are provided in order to fully illustrate preferred embodiments of the invention, and not to limit the scope of the invention. The scope of the invention is set forth in the appended claims. 
     I. Overview of Web Site and Recommendation Services 
     The Amazon.com Web site includes functionality for allowing users to search, browse, and make purchases from an online catalog of several million book titles, music titles, video titles, and other types of items. Using a shopping cart feature of the site, users can add and remove items to/from a personal shopping cart which is persistent over multiple sessions. (As used herein, a “shopping cart” is a data structure and associated code which keeps track of items that have been selected by a user for possible purchase.) For example, a user can modify the contents of the shopping cart over a period of time, such as one week, and then proceed to a check out area of the site to purchase the shopping cart contents. 
     The user can also create multiple shopping carts within a single account. For example, a user can set up separate shopping carts for work and home, or can set up separate shopping carts for each member of the user&#39;s family. A preferred shopping cart scheme for allowing users to set up and use multiple shopping carts is disclosed in U.S. application Ser. No. 09/104,942, filed Jun. 25, 1998, titled METHOD AND SYSTEM FOR ELECTRONIC COMMERCE USING MULTIPLE ROLES, the disclosure of which is hereby incorporated by reference. 
     The site also implements a variety of different recommendation services for recommending book titles, music titles, and/or video titles to users. One such service, known as BookMatcher™, allows users to interactively rate individual books on a scale of 1-5 to create personal item ratings profiles, and applies collaborative filtering techniques to these profiles to generate personal recommendations. The BookMatcher service is described in detail in U.S. application Ser. No. 09/040,171 filed Mar. 17, 1998, the disclosure of which is hereby incorporated by reference. The site may also include associated services that allow users to rate other types of items, such as CDs and videos. As described below, the ratings data collected by the BookMatcher service and similar services is optionally incorporated into the recommendation processes of the present invention. 
     Another type of service is a recommendation service which operates in accordance with the invention. The service (“Recommendation Service”) is preferably used to recommend book titles, music titles and/or videos titles to users, but could also be used in the context of the same Web site to recommend other types of items, including authors, artists, and groups or categories of titles. Briefly, given a unary listing of items that are “known” to be of interest to a user (e.g., a list of items purchased, rated, and/or viewed by the user), the Recommendation Service generates a list of additional items (“recommendations”) that are predicted to be of interest to the user. (As used herein, the term “interest” refers generally to a user&#39;s liking of or affinity for an item; the term “known” is used to distinguish items for which the user has implicitly or explicitly indicated some level of interest from items predicted by the Recommendation Service to be of interest.) 
     The recommendations are generated using a table which maps items to lists of “similar” items (“similar items lists”), without the need for users to rate any items (although ratings data may optionally be used). For example, if there are three items that are known to be of interest to a particular user (such as three items the user recently purchased), the service may retrieve the similar items lists for these three items from the table, and appropriately combine these lists (as described below) to generate the recommendations. 
     In accordance with one aspect of the invention, the mappings of items to similar items (“item-to-item mappings”) are generated periodically, such as once per week, from data which reflects the collective interests of the community of users. More specifically, the item-to-item mappings are generated by an off-line process which identifies correlations between known interests of users in particular items. For example, in the embodiment described in detail below, the mappings are generating by analyzing user purchase histories to identify correlations between purchases of particular items (e.g., items A and B are similar because a relatively large portion of the users that purchased item A also bought item B). The item-to-item mappings could also reflect other types of similarities, including content-based similarities extracted by analyzing item descriptions or content. 
     An important aspect of the Recommendation Service is that the relatively computation-intensive task of correlating item interests is performed off-line, and the results of this task (item-to-item mappings) are stored in a mapping structure for subsequent look-up. This enables the personal recommendations to be generated rapidly and efficiently (such as in real-time in response to a request by the user), without sacrificing breadth of analysis. 
     In accordance with another aspect of the invention, the similar items lists read from the table are appropriately weighted (prior to being combined) based on indicia of the user&#39;s affinity for or current interest in the corresponding items of known interest. For example, in one embodiment described below, if the item of known interest was previously rated by the user (such as through use of the BookMatcher service), the rating is used to weight the corresponding similar items list. Similarly, the similar items list for a book that was purchased in the last week may be weighted more heavily than the similar items list for a book that was purchased four months ago. 
     Another feature of the invention involves using the current and/or recent contents of the user&#39;s shopping cart as inputs to the Recommendation Service. For example, if the user currently has three items in his or her shopping cart, these three items can be treated as the items of known interest for purposes of generating recommendations, in which case the recommendations may be generated and displayed automatically when the user views the shopping cart contents. If the user has multiple shopping carts, the recommendations are preferably generated based on the contents of the shopping cart implicitly or explicitly designated by the user, such as the shopping cart currently being viewed. This method of generating recommendations can also be used within other types of recommendation systems, including content-based systems and systems that do not use item-to-item mappings. 
     Using the current and/or recent shopping cart contents as inputs tends to produce recommendations that are highly correlated to the current short-term interests of the user—even if these short term interests are not reflected by the user&#39;s purchase history. For example, if the user is currently searching for a father&#39;s day gift and has selected several books for prospective purchase, this method will have a tendency to identify other books that are well suited for the gift recipient. 
     Another feature of the invention involves generating recommendations that are specific to a particular shopping cart. This allows a user who has created multiple shopping carts to conveniently obtain recommendations that are specific to the role or purpose to the particular cart. For example, a user who has created a personal shopping cart for buying books for her children can designate this shopping cart to obtain recommendations of children&#39;s books. In one embodiment of this feature, the recommendations are generated based solely upon the current contents of the shopping cart selected for display. In another embodiment, the user may designate one or more shopping carts to be used to generate the recommendations, and the service then uses the items that were purchased from these shopping carts as the items of known interest. 
     As will be recognized by those skilled in the art, the above-described techniques for using shopping cart contents to generate recommendations can also be incorporated into other types of recommendation systems, including pure content-based systems. 
       FIG. 1  illustrates the basic components of the Amazon.com Web site  30 , including the components used to implement the Recommendation Service. The arrows in  FIG. 1  show the general flow of information that is used by the Recommendation Service. As illustrated by  FIG. 1 , the Web site  30  includes a Web server application  32  (“Web server”) which processes HTTP (Hypertext Transfer Protocol) requests received over the Internet from user computers  34 . The Web server  34  accesses a database  36  of HTML (Hypertext Markup Language) content which includes product information pages and other browsable information about the various products of the catalog. The “items” that are the subject of the Recommendation Service are the titles (regardless of media format such as hardcover or paperback) that are represented within this database  36 . 
     The Web site  30  also includes a “user profiles” database  38  which stores account-specific information about users of the site. Because a group of individuals can share an account, a given “user” from the perspective of the Web site may include multiple actual users. As illustrated by  FIG. 1 , the data stored for each user may include one or more of the following types of information (among other things) that can be used to generate recommendations in accordance with the invention: (a) the user&#39;s purchase history, including dates of purchase, (b) the user&#39;s item ratings profile (if any), (c) the current contents of the user&#39;s personal shopping cart(s), and (d) a listing of items that were recently (e.g., within the last six months) removed from the shopping cart(s) without being purchased (“recent shopping cart contents”). If a given user has multiple shopping carts, the purchase history for that user may include information about the particular shopping cart used to make each purchase; preserving such information allows the Recommendation Service to be configured to generate recommendations that are specific to a particular shopping cart. 
     As depicted by  FIG. 1 , the Web server  32  communicates with various external components  40  of the site. These external components  40  include, for example, a search engine and associated database (not shown) for enabling users to interactively search the catalog for particular items. Also included within the external components  40  are various order processing modules (not shown) for accepting and processing orders, and for updating the purchase histories of the users. 
     The external components  40  also include a shopping cart process (not shown) which adds and removes items from the users&#39; personal shopping carts based on the actions of the respective users. (The term “process” is used herein to refer generally to one or more code modules that are executed by a computer system to perform a particular task or set of related tasks.) In one embodiment, the shopping cart process periodically “prunes” the personal shopping cart listings of items that are deemed to be dormant, such as items that have not been purchased or viewed by the particular user for a predetermined period of time (e.g. two weeks). The shopping cart process also preferably generates and maintains the user-specific listings of recent shopping cart contents. 
     The external components  40  also include recommendation service components  44  that are used to implement the site&#39;s various recommendation services. Recommendations generated by the recommendation services are returned to the Web server  32 , which incorporates the recommendations into personalized Web pages transmitted to users. 
     The recommendation service components  44  include a BookMatcher application  50  which implements the above-described BookMatcher service. Users of the BookMatcher service are provided the opportunity to rate individual book titles from a list of popular titles. The book titles are rated according to the following scale:
         1=Bad!   2=Not for me   3=OK   4=Liked it   5=Loved it!       

     Users can also rate book titles during ordinary browsing of the site. As depicted in  FIG. 1 , the BookMatcher application  50  records the ratings within the user&#39;s items rating profile. For example, if a user of the BookMatcher service gives the book Into Thin Air a score of “5,” the BookMatcher application  50  would record the item (by ISBN or other identifier) and the score within the user&#39;s item ratings profile. The BookMatcher application  50  uses the users&#39; item ratings profiles to generate personal recommendations, which can be requested by the user by selecting an appropriate hyperlink. As described in detail below, the item ratings profiles are also used by an “Instant Recommendations” implementation of the Recommendation Service. 
     The recommendation services components  44  also include a recommendation process  52 , a similar items table  60 , and an off-line table generation process  66 , which collectively implement the Recommendation Service. As depicted by the arrows in  FIG. 1 , the recommendation process  52  generates personal recommendations based on information stored within the similar items table  60 , and based on the items that are known to be of interest (“items of known interest”) to the particular user. 
     In the embodiments described in detail below, the items of known interest are identified based on information stored in the user&#39;s profile, such as by selecting all items purchased by the user or all items in the user&#39;s shopping cart. In other embodiments of the invention, other types of methods or sources of information could be used to identify the items of known interest. For example, in a service used to recommend Web sites, the items (Web sites) known to be of interest to a user could be identified by parsing a Web server access log and/or by extracting URLs from the “favorite places” list of the user&#39;s Web browser. In a service used to recommend restaurants, the items (restaurants) of known interest could be identified by parsing the user&#39;s credit card records to identify restaurants that were visited more than once. 
     The various processes  50 ,  52 ,  66  of the recommendation services may run, for example, on one or more Unix or NT based workstations or physical servers (not shown) of the Web site  30 . The similar items table  60  is preferably stored as a B-tree data structure to permit efficient look-up, and may be replicated across multiple machines (together with the associated code of the recommendation process  52 ) to accommodate heavy loads. 
     II. Similar Items Table ( FIG. 1 ) 
     The general form and content of the similar items table  60  will now be described with reference to  FIG. 1 . As this table can take on many alternative forms, the details of the table are intended to illustrate, and not limit, the scope of the invention. 
     As indicated above, the similar items table  60  maps items to lists of similar items based at least upon the collective interests of the community of users. The similar items table  60  is preferably generated periodically (e.g., once per week) by the off-line table generation process  66 . The table generation process  66  generates the table  60  from data that reflects the collective interests of the community of users. In the embodiment described in detail herein, the similar items table is generated exclusively from the purchase histories of the community of users (as depicted in  FIG. 1 ). In other embodiments, the table  60  may additionally or alternatively be generated from other indicia of user-item interests, including indicia based on users viewing activities, shopping cart activities, and item rating profiles. For example, the table  60  could be built exclusively from the present and/or recent shopping cart contents of users. The similar items table  60  could also reflect non-collaborative type item similarities, including content-based similarities derived by comparing item contents or descriptions. 
     Each entry in the similar items table  60  is preferably in the form of a mapping of a popular item  62  to a corresponding list  64  of similar items (“similar items lists”). As used herein, a “popular” item is an item which satisfies some pre-specified popularity criteria. For example, in the embodiment described herein, an item is treated as popular of it has been purchased by more than 30 customers during the life of the Web site. Using this criteria produces a set of popular items (and thus a recommendation service) which grows over time. The similar items list  64  for a given popular item  62  may include other popular items. 
     In other embodiments involving sales of products, the table  60  may include entries for most or all of the products of the online merchant, rather than just the popular items. In the embodiment described herein, several different types of items (books, CDs, videos, etc.) are reflected within the same table  60 , although separate tables could alternatively be generated for each type of item. 
     Each similar items list  64  consists of the N (e.g., 20) items which, based on correlations between purchases of items, are deemed to be the most closely related to the respective popular item  62 . Each item in the similar items list  64  is stored together with a commonality index (“CI”) value which indicates the relatedness of that item to the popular item  62 , based on sales of the respective items. A relatively high commonality index for a pair of items ITEM A and ITEM B indicates that a relatively large percentage of users who bought ITEM A also bought ITEM B (and vice versa). A relatively low commonality index for ITEM A and ITEM B indicates that a relatively small percentage of the users who bought ITEM A also bought ITEM B (and vice versa). As described below, the similar items lists are generated, for each popular item, by selecting the N other items that have the highest commonality index values. Using this method, ITEM A may be included in ITEM B&#39;s similar items list even though ITEM B in not present in ITEM A&#39;s similar items list. 
     In the embodiment depicted by  FIG. 1 , the items are represented within the similar items table  60  using product IDs, such as ISBNs or other identifiers. Alternatively, the items could be represented within the table by title ID, where each title ID corresponds to a given “work” regardless of its media format. In either case, different items which correspond to the same work, such as the hardcover and paperback versions of a given book or the VCR cassette and DVD versions of a given video, are preferably treated as a unit for purposes of generating recommendations. 
     Although the recommendable items in the described system are in the form of book titles, music titles and videos titles, it will be appreciated that the underlying methods and data structures can be used to recommend a wide range of other types of items. For example, in the system depicted by  FIG. 1 , the Recommendation Service could also be used to recommend authors, artists, and categorizations or groups of works. 
     III. General Process for Generating Recommendations ( FIG. 2 ) 
     The general sequence of steps that are performed by the recommendation process  52  to generate a set of personal recommendations will now be described with reference to  FIG. 2 . This process, and the more specific implementations of the process depicted by  FIGS. 5 and 7  (described below), are intended to illustrate, and not limit, the scope of the invention. 
     The  FIG. 2  process is preferably invoked in real-time in response to an online action of the user. For example, in an Instant Recommendations implementation ( FIGS. 5 and 6 ) of the service, the recommendations are generated and displayed in real-time (based on the user&#39;s purchase history and/or item ratings profile) in response to selection by the user of a corresponding hyperlink, such as a hyperlink which reads “Instant Book Recommendations” or “Instant Music Recommendations.” In a shopping cart based implementation ( FIG. 7 ), the recommendations are generated (based on the user&#39;s current and/or recent shopping cart contents) in real-time when the user initiates a display of a shopping cart, and are displayed on the same Web page as the shopping cart contents. The Instant Recommendations and shopping cart based embodiments are described separately below under corresponding headings. 
     Any of a variety of other methods can be used to initiate the recommendations generation process and to display the recommendations to the user. For example, the recommendations can automatically be generated periodically and sent to the user by e-mail, in which case the e-mail listing may contain hyperlinks to the product information pages of the recommended items. Further, the personal recommendations could be generated in advance of any request or action by the user, and cached by the Web site  30  until requested. 
     As illustrated by  FIG. 2 , the first step (step  80 ) of the recommendations-generation process involves identifying a set of items that are of known interest to the user. The “knowledge” of the user&#39;s interest can be based on explicit indications of interest (e.g., the user rated the item highly) or implicit indications of interest (e.g., the user added the item to a shopping cart). Items that are not “popular items” within the similar items table  60  can optionally be ignored during this step. 
     In the embodiment depicted in  FIG. 1 , the items of known interest are selected from one or more of the following groups: (a) items in the user&#39;s purchase history (optionally limited to those items purchased from a particular shopping cart); (b) items in the user&#39;s shopping cart (or a particular shopping cart designated by the user), (c) items rated by the user (optionally with a score that exceeds a certain threshold, such as two), and (d) items in the “recent shopping cart contents” list associated with a given user or shopping cart. In other embodiments, the items of known interest may additionally or alternatively be selected based on the viewing activities of the user. For example, the recommendations process  52  could select items that were viewed by the user for an extended period of time and/or viewed more than once. Further, the user could be prompted to select items of interest from a list of popular items. 
     For each item of known interest, the service retrieves the corresponding similar items list  64  from the similar items table  60  (step  82 ), if such a list exists. If no entries exist in the table  60  for any of the items of known interest, the process  52  may be terminated; alternatively, the process could attempt to identify additional items of interest, such as by accessing other sources of interest information. 
     In step  84 , the similar items lists  64  are optionally weighted based on information about the user&#39;s affinity for the corresponding items of known interest. For example, a similar items list  64  may be weighted heavily if the user gave the corresponding popular item a rating of “5” on a scale or 1-5, or if the user purchased multiple copies of the item. Weighting a similar items list  64  heavily has the effect of increasing the likelihood that the items in that list we be included in the recommendations that are ultimately presented to the user. In one implementation described below, the user is presumed to have a greater affinity for recently purchased items over earlier purchased items. 
     The similar items lists  64  are preferably weighted by multiplying the commonality index values of the list by a weighting value. The commonality index values as weighted by any applicable weighting value are referred to herein as “scores.” In other embodiments, the recommendations may be generated without weighting the similar items lists  64 . 
     If multiple similar items lists  64  are retrieved in step  82 , the lists are appropriately combined (step  86 ), such as by merging the lists while summing the scores of like items. The resulting list is then sorted (step  88 ) in order of highest-to-lowest score. In step  90 , the sorted list is filtered to remove unwanted items. The items removed during the filtering process may include, for example, items that have already been purchased or rated by the user, and items that fall outside any product group (such as music or books), product category (such as non-fiction), or content rating (such as PG or adult) designated by the user. The filtering step could alternatively be performed at a different stage of the process, such as during the retrieval of the similar items lists from the table  60 . The result of step  90  is a list (“recommendations list”) of other items to be recommended to the user. 
     In step  92 , one or more additional items are optionally added to the recommendations list. In one embodiment, the items added in step  92  are selected from the set of items (if any) in the user&#39;s “recent shopping cart contents” list. As an important benefit of this step, the recommendations include one or more items that the user previously considered purchasing but did not purchase. The items added in step  92  may additionally or alternatively be selected using another recommendations method, such as a content-based method. 
     Finally, in step  94 , a list of the top M (e.g., 15) items of the recommendations list are returned to the Web server  32  ( FIG. 1 ). The Web server incorporates this list into one or more Web pages that are returned to the user, with each recommended item being presented as a hypertextual link to the item&#39;s product information page. The recommendations may alternatively be conveyed to the user by email, facsimile, or other transmission method. Further, the recommendations could be presented as advertisements for the recommended items. 
     IV. Generation of Similar Items Table ( FIGS. 3 and 4 ) 
     The table-generation process  66  is preferably executed periodically (e.g., once a week) to generate a similar items table  60  that reflects the most recent purchase history data. The recommendation process  52  uses the most recently generated version of the table  60  to generate recommendations. 
       FIG. 3  illustrates the sequence of steps that are performed by the table generation process  66  to build the similar items table  60 . The general form of temporary data structures that are generated during the process are shown at the right of the drawing. As will be appreciated by those skilled in the art, any of a variety of alternative methods could be used to generate the table  60 . 
     As depicted by  FIG. 3 , the process initially retrieves the purchase histories for all customers (step  100 ). Each purchase history is in the general form of the user ID of a customer together with a list of the product IDs (ISBNs, etc.) of the items (books, CDs, videos, etc.) purchased by that customer. In embodiments which support multiple shopping carts within a given account, each shopping cart could be treated as a separate customer for purposes of generating the table. For example, if a given user (or group of users that share an account) purchased items from two different shopping carts within the same account, these purchases could be treated as the purchases of separate users. 
     The product IDs may be converted to title IDs during this process, or when the table  60  is later used to generate recommendations, so that different versions of an item (e.g., hardcover and paperback) are represented as a single item. This may be accomplished, for example, by using a separate database which maps product IDs to title IDs. To generate a similar items table that strongly reflects the current tastes of the community, the purchase histories retrieved in step  100  can be limited to a specific time period, such as the last six months. 
     In steps  102  and  104 , the process generates two temporary tables  102 A and  104 A. The first table  102 A maps individual customers to the items they purchased. The second table  104 A maps items to the customers that purchased such items. To avoid the effects of “ballot stuffing,” multiple copies of the same item purchased by a single customer are represented with a single table entry. For example, even if a single customer purchased 4000 copies of one book, the customer will be treated as having purchased only a single copy. In addition, items that were sold to an insignificant number (e.g., &lt;15) of customers are preferably omitted or deleted from the tables  102 A,  104 B. 
     In step  106 , the process identifies the items that constitute “popular” items. This may be accomplished, for example, by selecting from the item-to-customers table  104 A those items that were purchased by more than a threshold number (e.g., 30) of customers. In the context of the Amazon.com Web site, to resulting set of popular items may contain hundreds of thousands or millions of items. 
     In step  108 , the process counts, for each (popular_item, other_item) pair, the number of customers that are in common. A pseudocode sequence for performing this step is listed in Table 1. The result of step  108  is a table that indicates, for each (popular_item, other_item) pair, the number of customers the two have in common. For example, in the hypothetical table  108 A of  FIG. 3 , POPULAR_A and ITEM_B have seventy customers in common, indicating that seventy customers bought both items. 
     
       
         
               
               
             
               
               
             
               
               
             
               
               
             
           
               
                   
                 TABLE 1 
               
               
                   
                   
               
             
             
               
                   
                 for each popular_item 
               
             
          
           
               
                   
                 for each customer in customers of item 
               
             
          
           
               
                   
                 for each other_item in items of customer 
               
             
          
           
               
                   
                 increment common-customer-count(popular_item, 
               
               
                   
                 other_item) 
               
               
                   
                   
               
             
          
         
       
     
     In step  110 , the process generates the commonality indexes for each (popular_item, other_item) pair in the table  108 A. As indicated above, the commonality index (CI) values are measures of the similarity between two items, with larger CI values indicating greater degrees of similarity. The commonality indexes are preferably generated such that, for a given popular_item, the respective commonality indexes of the corresponding other_items take into consideration both (a) the number of customers that are common to both items, and (b) the total number of customers of the other_item. A preferred method for generating the commonality index values is set forth in the equation below, in which N A  represents the number of customers of item_A, N B  represents the number of customers of item_B, and N common  represents the number of customers of item_A and item_B. 
     
       
         
           
             
               CI 
               ⁡ 
               
                 ( 
                 
                   item_A 
                   , 
                   item_B 
                 
                 ) 
               
             
             = 
             
               
                 N 
                 common 
               
               
                 
                   
                     N 
                     A 
                   
                   × 
                   
                     N 
                     B 
                   
                 
               
             
           
         
       
     
       FIG. 4  illustrates this method in example form. In the  FIG. 4  example, item_P (a popular item) has two “other items,” item_X and item_Y. Item_P has been purchased by 300 customers, item_X by 300 customers, and item_Y by 30,000 customers. In addition, item_P and item_X have 20 customers in common, and item_P and item_Y have 25 customers in common. Applying the equation above to the values shown in  FIG. 4  produces the following results:
 
 CI (item —   P ,item —   X )=20/sqrt(300×300))=0.0667
 
 CI (item —   P ,item —   Y )=25/sqrt(300×30,000))=0.0083
 
     Thus, even though items P and Y have more customers in common than items P and X, items P and X are treated as being more similar than items P and Y. This result desirably reflects the fact that the percentage of item_X customers that bought item_P (6.7%) is much greater than the percentage of item_Y customers that bought item_P (0.08%). 
     Because this equation is symmetrical (i.e., CI(item_A, item_B)=CI(item_B, item_A)), it is not necessary to separately calculate the CI value for every location in the table  108 A. In other embodiments, an asymmetrical method may be used to generate the CI values. For example, the CI value for a (popular_item, other_item) pair could be generated as (customers of popular_item and other_item)/(customers of other_item). 
     Following step  110  of  FIG. 3 , each popular item has a respective “other_items” list which includes all of the other_items from the table  108 A and their associated CI values. In step  112 , each other_items list is sorted from highest-to-lowest commonality index. Using the  FIG. 4  values as an example, item_X would be positioned closer to the top of the item_B&#39;s list than item_Y, since 0.014907&gt;0.001643. 
     In step  114 , the sorted other_items lists are filtered by deleting all list entries that have fewer than 3 customers in common. For example, in the other_items list for POPULAR_A in table  108 A, ITEM_A would be deleted since POPULAR_A and ITEM_A have only two customers in common. Deleting such entries tends to reduce statistically poor correlations between item sales. 
     In step  116 , the sorted other_items lists are truncated to length N to generate the similar items lists, and the similar items lists are stored in a B-tree table structure for efficient look-up. 
     As indicated above, any of a variety of other methods for evaluating similarities between items could be incorporated into the table generation process  66 . For example, the table generation process could compare item contents and/or use previously-assigned product categorizations as additional indicators of item similarities. An important benefit of the  FIG. 3  method, however, is that the items need not contain any content that is amenable to feature extraction techniques, and need not be pre-assigned to any categories. For example, the method can be used to generate a similar items table given nothing more than the product IDs of a set of products and user purchase histories with respect to these products. 
     Another important benefit of the Recommendation Service is that the bulk of the processing (the generation of the similar items table  60 ) is performed by an off-line process. Once this table has been generated, personalized recommendations can be generated rapidly and efficiently, without sacrificing breadth of analysis. 
     V. Instant Recommendations Service ( FIGS. 5 and 6 ) 
     A specific implementation of the Recommendation Service, referred to herein as the Instant Recommendations service, will now be described with reference to  FIGS. 5 and 6 . 
     As indicated above, the Instant Recommendations service is invoked by the user by selecting a corresponding hyperlink from a Web page. For example, the user may select an “Instant Book Recommendations” or similar hyperlink to obtain a listing of recommended book titles, or may select a “Instant Music Recommendations” or “Instant Video Recommendations” hyperlink to obtain a listing of recommended music or video titles. As described below, the user can also request that the recommendations be limited to a particular item category, such as “non-fiction,” “jazz” or “comedies.” The Instant Recommendations service generates the recommendations based exclusively on the purchase history and any item ratings profile of the particular user. The service becomes available to the user (i.e., the appropriate hyperlink is presented to the user) once the user has purchased and/or rated a threshold number (e.g. three) of popular items within the corresponding product group. If the user has established multiple shopping carts, the user may also be presented the option of designating a particular shopping cart to be used in generating the recommendations. 
       FIG. 5  illustrates the sequence of steps that are performed by the Instant Recommendations service to generate personal recommendations. Steps  180 - 194  in  FIG. 5  correspond, respectively, to steps  80 - 94  in  FIG. 2 . In step  180 , the process  52  identifies all popular items that have been purchased by the user (from a particular shopping cart, if designated) or rated by the user, within the last six months. In step  182 , the process retrieves the similar items lists  64  for these popular items from the similar items table  60 . 
     In step  184 , the process  52  weights each similar items list based on the duration since the associated popular item was purchased by the user (with recently-purchased items weighted more heavily), or if the popular item was not purchased, the rating given to the popular item by the user. The formula used to generate the weight values to apply to each similar items list is listed in C in Table 2. In this formula, “is purchased” is a boolean variable which indicates whether the popular item was purchased, “rating” is the rating value (1-5), if any, assigned to the popular item by the user, “order date” is the date/time (measured in seconds since 1970) the popular item was purchased, “now” is the current date/time (measured in seconds since 1970), and “6 months” is six months in seconds. 
     
       
         
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
             
             
               
                 1 
                 Weight = ( (is_purchased ? 5 : rating) * 2 − 5) * 
               
               
                 2 
                 ( 1 + (max( (is purchased ? order_date : 0) − (now − 6 months), 0 ) ) 
               
               
                 3 
                 / (6 months)) 
               
               
                   
               
             
          
         
       
     
     In line 1 of the formula, if the popular item was purchased, the value “5” (the maximum possible rating value) is selected; otherwise, the user&#39;s rating of the item is selected. The selected value (which may range from 1-5) is then multiplied by 2, and 5 is subtracted from the result. The value calculated in line 1 thus ranges from a minimum of −3 (if the item was rated a “1”) to a maximum of 5 (if the item was purchased or was rated a “5”). 
     The value calculated in line 1 is multiplied by the value calculated in lines 2 and 3, which can range from a minimum of 1 (if the item was either not purchased or was purchased at least six months ago) to a maximum of 2 (if order_date=now). Thus, the weight can range from a minimum of −6 to a maximum of 10. Weights of zero and below indicate that the user rated the item a “2” or below. Weights higher than 5 indicate that the user actually purchased the item (although a weight of 5 or less is possible even if the item was purchased), with higher values indicating more recent purchases. 
     The similar items lists  64  are weighted in step  184  by multiplying the CI values of the list by the corresponding weight value. For example, if the weight value for a given popular item is ten, and the similar items list  64  for the popular item is
         (productid_A, 0.10), (productid_B, 0.09), (productid_C, 0.08), . . .
 
the weighted similar items list would be:
   (productid_A, 1.0), (productid_B, 0.9), (productid_C, 0.8), . . .
 
The numerical values in the weighted similar items lists are referred to as “scores.”
       

     In step  186 , the weighted similar items lists are merged (if multiple lists exist) to form a single list. During this step, the scores of like items are summed. For example, if a given other_item appears in three different similar items lists  64 , the three scores (including any negative scores) are summed to produce a composite score. 
     In step  188 , the resulting list is sorted from highest-to-lowest score. The effect of the sorting operation is to place the most relevant items at the top of the list. In step  190 , the list is filtered by deleting any items that (1) have already been purchased or rated by the user, (2) have a negative score, or (3) do not fall within the designated product group (e.g., books) or category (e.g., “science fiction,” or “jazz”). 
     In step  192  one or more items are optionally selected from the recent shopping cart contents list (if such a list exists) for the user, excluding items that have been rated by the user or which fall outside the designated product group or category. The selected items, if any, are inserted at randomly-selected locations within the top M (e.g., 15) positions in the recommendations list. Finally, in step  194 , the top M items from the recommendations list are returned to the Web server  32 , which incorporates these recommendations into one or more Web pages. 
     The general form of such a Web page is shown in  FIG. 6 , which lists five recommended items. From this page, the user can select a link associated with one of the recommended items to view the product information page for that item. In addition, the user can select a “more recommendations” button  200  to view additional items from the list of M items. Further, the user can select a “refine your recommendations” link to rate or indicate ownership of the recommended items. Indicating ownership of an item causes the item to be added to the user&#39;s purchase history listing. 
     The user can also select a specific category such as “non-fiction” or “romance” from a drop-down menu  202  to request category-specific recommendations. Designating a specific category causes items in all other categories to be filtered out in step  190  ( FIG. 5 ). 
     VI. Shopping Cart Based Recommendations ( FIG. 7 ) 
     Another specific implementation of the Recommendation Service, referred to herein as shopping cart recommendations, will now be described with reference to  FIG. 7 . 
     The shopping cart recommendations service is preferably invoked automatically when the user displays the contents of a shopping cart that contains more than a threshold number (e.g., 1) of popular items. The service generates the recommendations based exclusively on the current contents of the shopping cart. As a result, the recommendations tend to be highly correlated to the user&#39;s current shopping interests. In other implementations, the recommendations may also be based on other items that are deemed to be of current interest to the user, such as items in the recent shopping cart contents of the user and/or items recently viewed by the user. Further, other indications of the user&#39;s current shopping interests could be incorporated into the process. For example, any search terms typed into the site&#39;s search engine during the user&#39;s browsing session could be captured and used to perform content-based filtering of the recommended items list. 
       FIG. 7  illustrates the sequence of steps that are performed by the shopping cart recommendations service to generate a set of shopping-cart-based recommendations. In step  282 , the similar items list for each popular item in the shopping cart is retrieved from the similar items table  60 . The similar items list for one or more additional items that are deemed to be of current interest could also be retrieved during this step, such as the list for an item recently deleted from the shopping cart or recently viewed for an extended period of time. 
     In step  286 , these similar items lists are merged while summing the commonality index (CI) values of like items. In step  288 , the resulting list is sorted from highest-to-lowest score. In step  290 , the list is filtered to remove any items that exist in the shopping cart or have been purchased or rated by the user. Finally, in step  294 , the top M (e.g., 5) items of the list are returned as recommendations. The recommendations are preferably presented to the user on the same Web page (not shown) as the shopping cart contents. 
     If the user has defined multiple shopping carts, the recommendations generated by the  FIG. 7  process may be based solely on the contents of the shopping cart currently selected for display. As described above, this allows the user to obtain recommendations that correspond to the role or purpose of a particular shopping cart (e.g., work versus home). 
     The various uses of shopping cart contents to generate recommendations as described above can be applied to other types of recommendation systems, including content-based systems. For example, the current and/or past contents of a shopping cart can be used to generate recommendations in a system in which mappings of items to lists of similar items are generated from a computer-based comparison of item contents. Methods for performing content-based similarity analyses of items are well known in the art, and are therefore not described herein. 
     Although this invention has been described in terms of certain preferred embodiments, other embodiments that are apparent to those of ordinary skill in the art are also within the scope of this invention. For example, although the embodiments described herein employ item lists, other programming methods for keeping track of and combining sets of similar items can be used. Accordingly, the scope of the present invention is intended to be defined only by reference to the appended claims. 
     In the claims which follow, reference characters used to denote process steps are provided for convenience of description only, and not to imply a particular order for performing the steps.

Technology Classification (CPC): 6