Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user. Advantageously, the web sites need not host the infrastructure for providing such content.

Abstract: Automated processes are disclosed for identifying bundles of three or more items to suggest to users. In one embodiment, item action data, such as item purchase data reflective of the item purchases of a user population, is used to detect pairwise (item to item) associations between particular items represented in an electronic catalog. These pairwise associations are then used (optionally in combination with other information) to select bundles of three or more items to recommend to users. The bundles may, for example, be suggested on item detail pages of the electronic catalog.

Abstract: Various processes are disclosed for discovering item relationships between particular items, such as products represented in an electronic catalog, based on monitored user behaviors (e.g., item viewing activities, item purchases, shopping cart activities, etc.). The discovered item relationships may, for example, be used to generate personalized item recommendations for users, and/or to supplement item detail pages of an electronic catalog with lists of related items. Also disclosed are processes for generating personalized item recommendations based on users' search activities and browse node visits.

Abstract: Various processes are disclosed for discovering item relationships between particular items, such as products represented in an electronic catalog, based on monitored user behaviors (e.g., item viewing activities, item purchases, shopping cart activities, etc.). The discovered item relationships may, for example, be used to generate personalized item recommendations for users, and/or to supplement item detail pages of an electronic catalog with lists of related items. Also disclosed are processes for generating personalized item recommendations based on users' search activities and browse node visits.

Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user. Advantageously, the web sites need not host the infrastructure for providing such content.

Abstract: Various processes are disclosed for discovering item relationships between particular items, such as products represented in an electronic catalog, based on monitored user behaviors (e.g., item viewing activities, item purchases, shopping cart activities, etc.). The discovered item relationships may, for example, be used to generate personalized item recommendations for users, and/or to supplement item detail pages of an electronic catalog with lists of related items. Also disclosed are processes for generating personalized item recommendations based on users' search activities and browse node visits.

Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user. Advantageously, the web sites need not host the infrastructure for providing such content.

Abstract: A computer system for detecting associations between items may include a probabilistic analysis component that accesses user data having information about item selections by a plurality of users. The probabilistic analysis component may programmatically generate associations between certain items by determining a first number of users who selected the items and by estimating a probability that a second number of users would have selected the items due to random chance. The probabilistic analysis component may estimate this probability based at least partly on a number of item selections made by one or more of the users.

Abstract: Data mining systems and methods are disclosed for generating data that is helpful to users in selecting between items represented in an electronic data repository, such as an electronic catalog. One disclosed data mining method generates pairwise comparison data for particular pairs of items. The pairwise comparison data for a given item pair reveals a tendency of users who consider both items in the pair to select one item over the other. The pairwise comparison data may be appropriately exposed to users of the electronic repository. For instance, an item detail page for item A may be supplemented with an indication that users who view both item A and item B select item B a specified percentage of the time. Another data mining method uses item viewing histories and item purchase histories of users in combination to identify pairs of items that are good candidates for being recommended in combination.

Abstract: Data mining systems and methods are disclosed for generating data that is helpful to users in selecting between items represented in an electronic data repository, such as an electronic catalog. One disclosed data mining method generates pairwise comparison data for particular pairs of items. The pairwise comparison data for a given item pair reveals a tendency of users who consider both items in the pair to select one item over the other. The pairwise comparison data may be appropriately exposed to users of the electronic repository. For instance, an item detail page for item A may be supplemented with an indication that users who view both item A and item B select item B a specified percentage of the time. Another data mining method uses item viewing histories and item purchase histories of users in combination to identify pairs of items that are good candidates for being recommended in combination.

Abstract: A recommendation system uses feedback from users on specific item recommendations to assess the quality of the recommendation rules used to generate such recommendations. The feedback may be explicit (e.g., a user rates a particular recommended item), implicit (e.g., a user purchases a recommended item), or both. The system may use these assessments to modify the degree to which particular recommendation rules are used to generate recommendations. For instance, if a particular recommendation rule leads to negative feedback relatively frequently, the system reduce or terminate its reliance on the rule. In some embodiments, the system may also increase its reliance on recommendation rules that tend to produce positive feedback.

Abstract: Data mining systems and methods are disclosed for generating data that is helpful to users in selecting between items represented in an electronic data repository, such as an electronic catalog. One disclosed data mining method generates pairwise comparison data for particular pairs of items based on aggregated user activity data reflective of item consideration actions and item selection actions of users. Another data mining method uses item viewing histories and item purchase histories of users in combination to assess whether particular items are good candidates for being purchased or recommended in combination. Statistical data resulting from one or both of these data mining methods may be presented to users (e.g., on electronic catalog pages) to assist users in making informed item selection decisions.

Abstract: A recommendation system uses feedback from users on specific item recommendations to assess the quality of the recommendation rules used to generate such recommendations. The feedback may be explicit (e.g., a user rates a particular recommended item), implicit (e.g., a user purchases a recommended item), or both. The system may use these assessments to modify the degree to which particular recommendation rules are used to generate recommendations. For instance, if a particular recommendation rule leads to negative feedback relatively frequently, the system reduce or terminate its reliance on the rule. In some embodiments, the system may also increase its reliance on recommendation rules that tend to produce positive feedback.

Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user.

Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user. Advantageously, the web sites need not host the infrastructure for providing such content.

Abstract: Various processes are disclosed for discovering item relationships between particular items, such as products represented in an electronic catalog, based on monitored user behaviors (e.g., item viewing activities, item purchases, shopping cart activities, etc.). The discovered item relationships may, for example, be used to generate personalized item recommendations for users, and/or to supplement item detail pages of an electronic catalog with lists of related items. Also disclosed are processes for generating personalized item recommendations based on users' search activities and browse node visits.

Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user. Advantageously, the web sites need not host the infrastructure for providing such content.

Abstract: Various methods are disclosed for monitoring user browsing activities, and for using such information to provide 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 service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user.

Abstract: A service is disclosed for enabling web sites and other entities to provide behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations, including associations between particular search queries and items. The detected associations are recorded in site-specific datasets, and are made available to the corresponding sites via the service's interface.