Abstract: A user device displays portions of an electronic publication for a user to read. The user device tracks the user's reading behavior of the portions of the electronic publication. The user device then suggests additional reading material for the user based on the user's reading behavior.

Abstract: Electronic content items such as electronic books are enhanced by identifying citations within the content items, identifying sources of the objects of the citations, and associating the citations with such sources so that readers of the content items can easily purchase or otherwise obtain the citation objects. The citations may be updated as new products become available or information related to the products changes over time.

Abstract: An interactive system generates personalized item recommendations for users based partly or wholly on ratings assigned by the users to particular items. The system includes an item rating user interface that enables a user to view, prior to rating an item, information regarding the types of items that will be recommended to the user if the user assigns a particular rating or type of rating to the item. The user interface thereby enables users to refrain from performing rating actions that will tend to result in low utility or “poor quality” recommendations from the users' perspectives.

Abstract: A user device displays portions of an electronic publication for a user to read. The user device tracks the user's reading behavior of the portions of the electronic publication. The user device then suggests additional reading material for the user based on the user's reading behavior.

Abstract: A user device displays portions of an electronic publication for a user to read. The user device tracks the user's reading behavior of the portions of the electronic publication. The user device then selects a problem from a group of pre-generated problems for the user based on the user's reading behavior and provides the selected problem to the user.

Abstract: A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.

Abstract: An interactive system generates personalized item recommendations for users based partly or wholly on ratings assigned by the users to particular items. The system includes an item rating user interface that enables a user to view, prior to rating an item, information regarding the types of items that will be recommended to the user if the user assigns a particular rating or type of rating to the item. The user interface thereby enables users to refrain from performing rating actions that will tend to result in low utility or “poor quality” recommendations from the users' perspectives.

Abstract: Various computer-implemented processes are disclosed for using item clustering methods in the process of generating personalized item recommendations for users. One process involves applying a clustering algorithm to a user's collection of items, and using information about the resulting clusters to select items to use as recommendation sources. Personalized recommendations may then be generated based on the selected source items. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to rate entire clusters of items. The resulting cluster ratings may be used to select recommendation sources, and/or may otherwise be considered in generating recommendations for the user. Cluster-based processes are also disclosed for filtering and organizing the output of a recommendation engine.

Abstract: An interactive system generates personalized item recommendations for users based partly or wholly on ratings assigned by the users to particular items. The system includes an item rating user interface that enables a user to view, prior to rating an item, information regarding the types of items that will be recommended to the user if the user assigns a particular rating or type of rating to the item. The user interface thereby enables users to refrain from performing rating actions that will tend to result in low utility or “poor quality” recommendations from the users' perspectives.

Abstract: A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.

Abstract: Various computer-implemented processes are disclosed for using item clustering methods in the process of generating personalized item recommendations for users. One process involves applying a clustering algorithm to a user's collection of items, and using information about the resulting clusters to select items to use as recommendation sources. Personalized recommendations may then be generated based on the selected source items. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to rate entire clusters of items. The resulting cluster ratings may be used to select recommendation sources, and/or may otherwise be considered in generating recommendations for the user. Cluster-based processes are also disclosed for filtering and organizing the output of a recommendation engine.

Abstract: Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

Abstract: Computer-implemented processes are disclosed for clustering items, and for using item clusters to generate and/or present item recommendations. One process involves calculating distances between items based on how the items are categorized within a hierarchical browse structure. These distance calculations may then be used as a basis for forming clusters of items.

Abstract: Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

Abstract: A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.

Abstract: Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

Abstract: Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

Abstract: A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.

Abstract: A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.

Abstract: A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.