PROFILE MODELING FOR SHARING INDIVIDUAL USER PREFERENCES
First Claim
1. A computer-implemented method for creating a compact, machine-usable user taste profile comprising the steps of:
- accessing an associational knowledge base AKB that stores relationships among a catalog of items in computer-usable form, the AKB including identification of a plurality of categories, wherein each category is a subset of the catalog of items, and the categories are selected based on similarity among the items within a category;
providing an application for use by users, wherein the application uses the AKB to provide services to the users;
acquiring interaction data showing multiple users'"'"' interaction events with the items in the AKB;
analyzing the acquired interaction data so as to define a set of profile factors for describing the users'"'"' interactions, wherein each profile factor is a subset of the AKB categories;
forming a taste profile expressed as a weighted combination of the defined profile factors; and
storing the taste profile as a file, vector, table or other machine-usable data structure.
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Accused Products
Abstract
A computer-implemented method (FIG. 4), systems (FIG. 6) and data structures (420, 466) are disclosed for creating and exchanging a compact, machine-usable user taste profile (140,416,608). The method may include accessing an associational knowledge base “AKB” (124,406) that stores relationships among a catalog of items in computer-usable form. The AKB includes identification of a plurality of “categories” (304,306,310) wherein each category is a subset of the catalog of items (300), and the categories are defined based on similarity among the items within a category. User interactions (126,410) with an application (404) driven by an AKB (406) are analyzed relative to the categorization (412,414,416) by application of profile factors (450) to estimate a user profile (416). The user profile can be exported to other applications that are driven by a compatible AKB in order to provide an experience tailored to the user'"'"'s individual taste preferences.
124 Citations
31 Claims
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1. A computer-implemented method for creating a compact, machine-usable user taste profile comprising the steps of:
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accessing an associational knowledge base AKB that stores relationships among a catalog of items in computer-usable form, the AKB including identification of a plurality of categories, wherein each category is a subset of the catalog of items, and the categories are selected based on similarity among the items within a category; providing an application for use by users, wherein the application uses the AKB to provide services to the users; acquiring interaction data showing multiple users'"'"' interaction events with the items in the AKB; analyzing the acquired interaction data so as to define a set of profile factors for describing the users'"'"' interactions, wherein each profile factor is a subset of the AKB categories; forming a taste profile expressed as a weighted combination of the defined profile factors; and storing the taste profile as a file, vector, table or other machine-usable data structure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computer-implemented method for personalizing applications driven by knowledge bases, comprising:
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accessing a first associational knowledge base AKB-1 that stores relationships among a first catalog of items U-1 in computer-usable form, the AKB-1 including identification of a first set of categories C-1, wherein each category of C-1 is a subset of the first catalog of items U-1, and the categories are selected based on similarity among the items of U-1 within a category; accessing a second associational knowledge base AKB-2 that stores relationships among a second catalog of items U-2 in computer-usable form, the AKB-2 including identification of a second set of categories C-2, wherein each category of C-2 is a subset of the second catalog of items U-2, and the categories are selected based on similarity among the items of U-2 within a category; acquiring interaction data showing user interaction events with the items in the first AKB-1; analyzing the acquired interaction data so as to define a first set of profile factors for the first AKB-1, wherein each profile factor is a subset of the AKB-1 set of categories C-1; forming a first taste profile expressed as a weighted combination of the defined profile factors; storing the taste profile as a file, vector, table or other machine-usable data structure; comparing the first and second sets of categories C-1, C-2 to identify categories in common; and if the number of categories in common to AKB-1 and AKB-2 exceeds a selected threshold, exporting the first taste profile for use by an application program driven by the second AKB-2, wherein the threshold number of common categories is chosen as sufficient for the application. - View Dependent Claims (15, 16, 17, 18)
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19. A system comprising:
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a first web interface to acquire interaction data from a first web service for a specific user m, wherein the first web service is enabled to store interaction data that reflects user m interaction events with a catalog of items that are represented in a selected associational knowledge base AKB; a user profiling web application program executable on a server and coupled to receive the user m interaction event data from the first web service, and from that data to form a user m taste profile expressed as a weighted vector of predetermined profile factors associated with the AKB; and a second web interface to download the user m taste profile to a second web service to enable the second web service to provide improved services to user m based on the taste profile. - View Dependent Claims (20, 21, 22, 23)
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24. A user taste profile data structure comprising:
a collection of relative weights, each weight corresponding to a respective one of a predetermined set of profile factors relative to the knowledge stored in an associational knowledge base, wherein the taste profile data structure comprises one of a file, a vector, and a database table. - View Dependent Claims (25)
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26. A user taste profile data structure comprising:
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a collection of relative weights, each weight corresponding to a respective one of a predetermined set of profile factors relative to the knowledge stored in an associational knowledge base; and a collection of profile factors relative to an associational knowledge base, wherein each profile factor wherein each profile factor is a subset of the AKB categories; and wherein the relative weights, and the corresponding profile factors, are stored together in a user taste profile data structure comprising one of a file, a vector, and a database table. - View Dependent Claims (27)
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28. A computer program product for generating and distributing individual user taste profiles across the internet, the computer program product comprising a computer-readable storage medium containing executable computer program code for performing a method comprising:
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accessing an associational knowledge base AKB that stores relationships among a catalog of items in computer-usable form, the AKB including identification of a plurality of categories, wherein each category is a subset of the catalog of items, and the categories are selected based on similarity among the items within a category; identifying an application, wherein the application uses the AKB to provide services to users; acquiring from the application program and storing in memory interaction event data showing multiple users'"'"' interaction events with the items in the AKB; analyzing the interaction data so as to define a set of profile factors for describing the users'"'"' interactions, wherein each profile factor is a subset of the AKB categories; selecting the interaction event data for a specific individual user; forming a taste profile of the individual user, expressed as a weighted vector of the profile factors; and storing the individual user taste profile as a file, vector or other machine-usable data structure. - View Dependent Claims (29, 30, 31)
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Specification