Abstract: Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products, or other items, represented in an electronic catalog, and for using such information to identify items that are related to one another. In one embodiment, relationships between items within an electronic catalog are determined by identifying items that are frequently viewed by users within the same browsing session (e.g., items A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data may be stored in a table that maps items to sets of related items. The table may be used to provide personalized item recommendations to users, and/or to supplement item detail pages of the electronic catalog with lists of related items. In one embodiment, the table is used to provide session-specific item recommendations to users.

Abstract: A system provides recommendations of web sites, web pages, and/or products to a user based on web pages viewed during a current browsing session. In one embodiment, a browser plug-in or other client program monitors and reports information regarding browsing activities of users across multiple web sites. The resulting cross-site browse histories of the users are analyzed on an aggregated basis to detect behavior-based associations between particular sites, pages and/or products. The detected associations are in turn used to provide personalized recommendations to users. The associations and recommendations may also be based on an automated analysis of the content of the web pages represented in the users' browse histories.

Abstract: A computer-implemented service recommends items to a user based on items previously selected by the user, such as items previously purchased, viewed, or placed in an electronic shopping cart by the user. The items may, for example, be products represented within a database of an online merchant. In one embodiment, the service generates the recommendations using a previously generated table that maps items to respective lists of “similar” items. To generate the table, historical data indicative of users' affinities for particular items is processed periodically to identify correlations between item interests of users (e.g., items A and B are similar because a large portion of those who selected A also selected B). Personal recommendations are generated by accessing the table to identify items similar to those selected by the user. In one embodiment, items are recommended based on the current contents of a user's shopping cart.

Abstract: Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products or other items, and for using such information to identify items that are related to one another. In one embodiment, relationships between products within an online catalog are determined by identifying products that are frequently viewed by users within the same browsing session (e.g., products A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data is preferably stored in a table that maps items to sets of related items. The table may be used to provide personalized product recommendations to users, and/or to supplement product detail pages with lists of related products. In one embodiment, the table is used to provide session-specific product recommendations to users that are based on the products viewed by the user during the current browsing session.

Abstract: Various methods are disclosed for monitoring user browsing activities, and for using such information to provide session-specific item recommendations to users. In one embodiment, a monitoring component of a merchant's Web site maintains a record of products viewed by each user during a current browsing session—preferably based on visits to product detail pages. A recommendations component uses the resulting history of viewed products to identify additional products to recommend, preferably using a pre-existing table that maps products to related products. In one embodiment, this table is generated by periodically analyzing user browsing histories to identify correlations between purchases, viewing events, and/or other actions performed with respect to particular products. The recommended items may be displayed together with an option to individually deselect the recently viewed items on which the recommendations are based.

Abstract: An improved user interface and method are provided for presenting recommendations to a user when the user adds an item to a shopping cart. In response to the shopping cart add event, a page generation process generates and returns a page that includes a recommendations portion and a condensed view of the shopping cart. The recommendations portion preferably includes multiple recommendation sections, each of which displays a different respective set of recommended items selected according to a different respective recommendation or selection algorithm (e.g., recommendations based on shopping cart contents, recommendations based on purchase history, etc.). The condensed shopping cart view preferably lacks controls for editing the shopping cart, and lacks certain types of product information, making more screen real estate available for the display of the recommendations content. A link to a full shopping cart page allows the user to edit the shopping cart and view expanded product descriptions.

Abstract: A system provides session-specific web page or web site recommendations to a user based upon an identification of web pages previously viewed by the user during a browsing session. During a sequence of proximately visited locations, users tend to view web pages with similar content. To collect data, a client program executes in conjunction with a web browser on each of multiple users' computers. Each client program identifies pages viewed by the user and transmits the sequence of identifications to a server application executing on a recommendation system. The recommendation system creates tables of similar web pages based upon the sequences of locations visited by users. To create session-specific web page recommendations, the system uses the client program to identify a set of locations visited by the user during the session. The system then identifies similar web pages based upon the created tables and combines, sorts, and filters the results.

Abstract: In a Web site system in which different private records or other resources are personal to different users, a method is provided for allowing users to securely access a private resource without the need to enter a username, password, or other authentication information, and without the need to download special authentication software or data to the user's computer. Each resource is assigned a private uniform resource locator (URL) which includes a fixed character string and a unique token, and the URLs are conveyed by email (preferably using hyperlinks) to users that are entitled to access such resources. The tokens are generated using a method which distributes the tokens substantially randomly over the range of allowable token values (“token space”). The token space is selected to be sufficiently large relative to the expected number of valid tokens to inhibit the identification of valid tokens through trial and error.

Abstract: Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products or other items, and for using such information to identify items that are related to one another. In one embodiment, relationships between products within an online catalog are determined by identifying products that are frequently viewed by users within the same browsing session (e.g., products A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data is preferably stored in a table that maps items to sets of related items. The table may be used to provide personalized product recommendations to users, and/or to supplement product detail pages with lists of related products. In one embodiment, the table is used to provide session-specific product recommendations to users that are based on the products viewed by the user during the current browsing session.

Abstract: Various methods are disclosed for monitoring user browsing activities, and for using such information to provide session-specific item recommendations to users. In one embodiment, a monitoring component of a merchant's Web site maintains a record of products viewed by each user during a current browsing session—preferably based on visits to product detail pages. A recommendations component uses the resulting history of viewed products to identify additional products to recommend, preferably using a pre-existing table that maps products to related products. In one embodiment, this table is generated by periodically analyzing user browsing histories to identify correlations between purchases, viewing events, and/or other actions performed with respect to particular products. The recommended items may be displayed together with an option to individually deselect the recently viewed items on which the recommendations are based.

Abstract: A computer-implemented service recommends products or other items to a user based on a set of items known to be of interest to the user, such as a set of items currently in the user's electronic shopping cart. In one embodiment, the service identifies items that are currently in the user's shopping cart, and uses these items to generate a list of additional items that are predicted to be of interest to the user, wherein an additional item is selected to include in the list based in-part upon whether that item is related to more than one of the items in the user's shopping cart. The item relationships are preferably determined by an off-line process that analyzes user purchase histories to identify correlations between item purchases. The additional items are preferably displayed to the user when the user views the contents of the shopping cart.

Abstract: A computer-implemented service recommends items to a user based on items previously selected by the user, such as items previously purchased, viewed, or placed in an electronic shopping cart by the user. The items may, for example, be products represented within a database of an online merchant. In one embodiment, the service generates the recommendations using a previously generated table that maps items to respective lists of “similar” items. To generate the table, historical data indicative of users' affinities for particular items is processed periodically to identify correlations between item interests of users (e.g., items A and B are similar because a large portion of those who selected A also selected B). Personal recommendations are generated by accessing the table to identify items similar to those selected by the user. In one embodiment, items are recommended based on the current contents of a user's shopping cart.

Abstract: A recommendations service recommends items to individual users based on a set of items that are known to be of interest to the user, such as a set of items previously purchased by the user. In the disclosed embodiments, the service is used to recommend products to users of a merchant's Web site. The service generates the recommendations using a previously-generated table which maps items to lists of “similar” items. The similarities reflected by the table are based on the collective interests of the community of users. For example, in one embodiment, the similarities are based on correlations between the purchases of items by users (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 table also includes scores which indicate degrees of similarity between individual items.