Patents by Inventor Kushal Chakrabarti
Kushal Chakrabarti has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 8751507Abstract: 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.Type: GrantFiled: June 29, 2007Date of Patent: June 10, 2014Assignee: Amazon Technologies, Inc.Inventors: Sung H. Kim, Shing Yan Lam, Kushal Chakrabarti, George M. Ionkov, Brett W. Witt
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Patent number: 8560545Abstract: 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.Type: GrantFiled: January 3, 2012Date of Patent: October 15, 2013Assignee: Amazon Technologies, Inc.Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov
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Patent number: 8301623Abstract: A recommendations system uses probabilistic methods to select, from a candidate set of items, a set of items to recommend to a target user. The methods can effectively introduce noise into the recommendations process, causing the recommendations presented to the target user to vary in a controlled manner from one visit to the next. The methods may increase the likelihood that at least some of the items recommended over a sequence of visits will be useful to the target user. Some embodiments of the methods are stateless such that the system need not keep track of which items have been recommended to which users.Type: GrantFiled: May 22, 2007Date of Patent: October 30, 2012Assignee: Amazon Technologies, Inc.Inventors: Kushal Chakrabarti, Brent Smith
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Patent number: 8260787Abstract: 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.Type: GrantFiled: June 29, 2007Date of Patent: September 4, 2012Assignee: Amazon Technologies, Inc.Inventors: Shing Yan Lam, Kushal Chakrabarti, George M. Ionkov, Sung H. Kim, Brett W. Witt
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Publication number: 20120109778Abstract: 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.Type: ApplicationFiled: January 3, 2012Publication date: May 3, 2012Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov
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Publication number: 20120078747Abstract: 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.Type: ApplicationFiled: December 5, 2011Publication date: March 29, 2012Inventors: Kushal Chakrabarti, Brent R. Smith
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Patent number: 8095521Abstract: 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.Type: GrantFiled: March 30, 2007Date of Patent: January 10, 2012Assignee: Amazon Technologies, Inc.Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov
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Patent number: 8090621Abstract: 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.Type: GrantFiled: June 27, 2007Date of Patent: January 3, 2012Assignee: Amazon Technologies, Inc.Inventors: Kushal Chakrabarti, Brent R. Smith
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Patent number: 8019766Abstract: 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.Type: GrantFiled: March 30, 2007Date of Patent: September 13, 2011Assignee: Amazon Technologies, Inc.Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov
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Method, system, and medium for cluster-based categorization and presentation of item recommendations
Patent number: 7966225Abstract: 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.Type: GrantFiled: March 30, 2007Date of Patent: June 21, 2011Assignee: Amazon Technologies, Inc.Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov -
Patent number: 7949659Abstract: 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.Type: GrantFiled: June 29, 2007Date of Patent: May 24, 2011Assignee: Amazon Technologies, Inc.Inventors: Kushal Chakrabarti, James D. Chan, George M. Ionkov, Sung H. Kim, Shing Yan Lam, Brett W. Witt
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Patent number: 7743059Abstract: 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.Type: GrantFiled: March 30, 2007Date of Patent: June 22, 2010Assignee: Amazon Technologies, Inc.Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov
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Patent number: 7689457Abstract: 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.Type: GrantFiled: March 30, 2007Date of Patent: March 30, 2010Assignee: Amazon Technologies, Inc.Inventors: James D. Chan, Kushal Chakrabarti, George M. Ionkov
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Patent number: 7584159Abstract: Strategies are described for generating recommendations. The strategies generate a set of original recommendations based on a source of information. The strategies then transform the set of original recommendations into a set of similarity-spaced recommendations based on “repulsion force” analysis applied to the set of original recommendations. In a first implementation, the set of spaced recommendations represent a diverse sampling of items in the set of original recommendations. In a second implementation, the set of spaced recommendations represent a sampling of items in the set of original recommendations which omits or excludes recommendations assessed as obvious. A third implementation can combine the first and second implementations.Type: GrantFiled: October 31, 2005Date of Patent: September 1, 2009Assignee: Amazon Technologies, Inc.Inventors: Kushal Chakrabarti, Ron Kohavi, Brent R. Smith
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Patent number: 7542951Abstract: Strategies are described for generating recommendations. The strategies generate a set of original recommendations based on a source of information. The strategies then transform the set of original recommendations into a set of similarity-spaced recommendations based on “repulsion force” analysis applied to the set of original recommendations. In a first implementation, the set of spaced recommendations represent a diverse sampling of items in the set of original recommendations. In a second implementation, the set of spaced recommendations represent a sampling of items in the set of original recommendations which omits or excludes recommendations assessed as obvious. A third implementation can combine the first and second implementations.Type: GrantFiled: October 31, 2005Date of Patent: June 2, 2009Assignee: Amazon Technologies, Inc.Inventors: Kushal Chakrabarti, Ron Kohavi, Brent R. Smith
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Patent number: 7539632Abstract: The present disclosure provides systems and methods for identifying the product pages of an interactive catalog receiving at least one click-through originating from a commentary page and generating a list of products corresponding to the product pages. The method including determining a total number of click-throughs to each of the product pages from the commentary pages during a predetermined period of time and ordering the list in descending order based on the total number of click-throughs. Additionally, the method includes causing the display of the list.Type: GrantFiled: September 26, 2007Date of Patent: May 26, 2009Assignee: Amazon Technologies, Inc.Inventors: Kushal Chakrabarti, John D. Rodgers, Christel C. Berg
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Publication number: 20090006374Abstract: 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.Type: ApplicationFiled: June 29, 2007Publication date: January 1, 2009Inventors: Sung H. Kim, Shing Yan Lam, Kushal Chakrabarti, George M. Ionkov, Brett W. Witt
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Publication number: 20090006398Abstract: 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.Type: ApplicationFiled: June 29, 2007Publication date: January 1, 2009Inventors: Shing Yan Lam, Kushal Chakrabarti, George M. Ionkov, Sung H. Kim, Brett W. Witt
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Publication number: 20090006373Abstract: 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.Type: ApplicationFiled: June 29, 2007Publication date: January 1, 2009Inventors: Kushal Chakrabarti, James D. Chan, George M. Ionkov, Sung H. Kim, Shing Yan Lam, Brett W. Witt
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Publication number: 20080294617Abstract: A recommendations system uses probabilistic methods to select, from a candidate set of items, a set of items to recommend to a target user. Some embodiments of the methods effectively introduce noise into the recommendations process, causing the recommendations presented to the target user to vary in a controlled manner from one visit to the next. The methods may increase the likelihood that at least some of the items recommended over a sequence of visits will be useful to the target user. Some embodiments of the methods are stateless such that the system need not keep track of which items have been recommended to which users.Type: ApplicationFiled: May 22, 2007Publication date: November 27, 2008Inventors: Kushal Chakrabarti, Brent Smith