Abstract: Systems and processes are described for generating a generic relationship dataset that can be adapted for use by multiple domain-specific applications. Such applications can include recommendation applications, search applications, optimization applications, and/or other applications. These systems and processes can analyze some or all actions of users with respect to a network application, such as a web application. Data representing user actions occurring in a time window can be paired together in the relationship dataset, together with one or more metrics related to the user actions. These metrics can represent general measurements that in certain embodiments are not customized for an individual application. As a result, separate domain-specific applications can independently analyze the relationship data according to different criteria without maintaining separate sets of the relationship data.

Abstract: Generally described, embodiments of the present disclosure are directed toward the identification of items for inclusion in a recommendations list that may be displayed concurrently with an item selected by a user or users. The recommended items may be items that are of potential interest to the user and/or may be items that are related to the selected item. More specifically, embodiments of the present disclosure provide a method and system for generating one or more recommendations lists, providing those lists to users, evaluating users' interactions with those lists, and modifying the engines or techniques used to identify items that are to be included in the recommendations lists.

Abstract: Systems and processes are described for generating a generic relationship dataset that can be adapted for use by multiple domain-specific applications. Such applications can include recommendation applications, search applications, optimization applications, and/or other applications. These systems and processes can analyze some or all actions of users with respect to a network application, such as a web application. Data representing user actions occurring in a time window can be paired together in the relationship dataset, together with one or more metrics related to the user actions. These metrics can represent general measurements that in certain embodiments are not customized for an individual application. As a result, separate domain-specific applications can independently analyze the relationship data according to different criteria without maintaining separate sets of the relationship data.

Abstract: Generally described, embodiments of the present disclosure are directed toward the identification of items for inclusion in a recommendations list that may be displayed concurrently with an item selected by a user or users. The recommended items may be items that are of potential interest to the user and/or may be items that are related to the selected item. More specifically, embodiments of the present disclosure provide a method and system for generating one or more recommendations lists, providing those lists to users, evaluating users' interactions with those lists, and modifying the engines or techniques used to identify items that are to be included in the recommendations lists.

Abstract: Generally described, embodiments of the present disclosure are directed toward the identification of items for inclusion in a recommendations list that may be displayed concurrently with an item selected by a user or users. The recommended items may be items that are of potential interest to the user and/or may be items that are related to the selected item. More specifically, embodiments of the present disclosure provide a method and system for generating one or more recommendations lists, providing those lists to users, evaluating users' interactions with those lists, and modifying the engines or techniques used to identify items that are to be included in the recommendations lists.

Abstract: Behavior-based associations, such as item-to-item or query-to-item associations, are extrapolated to other items to create new associations. The items to which the associations are extrapolated may be “behavior deficient” items, or items for which the quantity of collected user activity data is insufficient to create meaningful or reliable behavior-based associations. The behavior-based associations are extrapolated based on content-based associations, or another type of “substitutability” association, between items. The items can be any type of item (e.g., products, web sites, documents, etc.) for which user behaviors (e.g., purchases, accesses, downloads, etc.) can be monitored and analyzed to detect behavior-based associations, and for which item content or other available information can be used to assess item substitutability.

Abstract: Behavior-based associations are extrapolated to items for which the quantity of collected user activity data is insufficient to create meaningful or reliable behavior-based associations (“behavior-deficient” items). The behavior-based associations are extrapolated based on content-based associations, or another type of “substitutability” association, between the behavior-deficient items and other items. The items can be any type of item for which user behaviors (e.g., purchases, accesses, downloads, etc.) can be monitored and analyzed to detect behavior-based associations, and for which item content or other available information can be used to assess item substitutability. For example, the items can be products represented in an electronic catalog, web pages or other documents accessible on a network, or web sites.

Abstract: Behavior-based associations, such as item-to-item or query-to-item associations, are extrapolated to other items to create new associations. The items to which the associations are extrapolated may be “behavior deficient” items, or items for which the quantity of collected user activity data is insufficient to create meaningful or reliable behavior-based associations. The behavior-based associations are extrapolated based on content-based associations, or another type of “substitutability” association, between items. The items can be any type of item (e.g., products, web sites, documents, etc.) for which user behaviors (e.g., purchases, accesses, downloads, etc.) can be monitored and analyzed to detect behavior-based associations, and for which item content or other available information can be used to assess item substitutability.

Abstract: An attribute of a first item is extrapolated to a second item that is not known to have that attribute. The extrapolation occurs as a result of a substitution association detected between the first and second items. The substitution association may be detected based on an analysis of the content of the first and second items. The extrapolated attribute may be a behavioral association with a third item, in which case an inference is drawn that the second and third items are behaviorally related. The items may, for example, be products represented in an electronic catalog.

Abstract: Behavior-based associations are extrapolated to items for which the quantity of collected user activity data is insufficient to create meaningful or reliable behavior-based associations (“behavior-deficient” items). The behavior-based associations are extrapolated based on content-based associations, or another type of “substitutability” association, between the behavior-deficient items and other items. The items can be any type of item for which user behaviors (e.g., purchases, accesses, downloads, etc.) can be monitored and analyzed to detect behavior-based associations, and for which item content or other available information can be used to assess item substitutability. For example, the items can be products represented in an electronic catalog, web pages or other documents accessible on a network, or web sites.