METHODS AND SYSTEMS FOR SELECTING AND PRESENTING CONTENT BASED ON A COMPARISON OF PREFERENCE SIGNATURES FROM MULTIPLE USERS

• US 20130167169A1
• Filed: 02/15/2013
• Published: 06/27/2013
• Est. Priority Date: 03/06/2006
• Status: Active Grant

• Current Assignee: Veveo, Inc. (Adeia Inc.)
• Original Assignee: Veveo, Inc. (Adeia Inc.)
• Inventors: ARAVAMUDAN, Murali, RAJASEKHARAN, Ajit, RAMAKRISHNAN, Kajamalai G.
• Granted Patent: US 9,128,987 B2
• Time in Patent Office: Days
• US Class Current: 725/13
• CPC Class Codes: G06F 16/152 using file content signatures, e.g. hash values
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/437 Administration of user profiles, e.g. generation, initialisation, adaptation, distribution
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 16/686 using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/44224 : Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4524 : involving the geographical location of the client retrieval from the Internet by querying based on geographical locations G06F16/9537; systems specially adapted for using geographical information in broadcast systems H04H60/70; protocols in which the network application is adapted for the location of the user terminal in communication control or processing H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00
• CPC Class Codes: H04N 21/4532 : involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/466 : Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4828 : for searching program descriptors retrieval of video data G06F16/739
• CPC Class Codes: Y10S 707/99933 : Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 : Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99937 : Sorting
• CPC Class Codes: Y10S 707/99943 : Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99945 : Object-oriented database structure processing

Abstract

A method of selecting and presenting content based on a comparison of preference signature from multiple users is provided. The method includes providing a set of content items having associated descriptive terms and receiving search input from users. The method includes presenting a subset of content items, receiving content selection actions, and learning users'"'"' preferences, wherein the preferences are weighted based on content items usage so that the user preferences are specified as a set of descriptive terms and associated weights. The method includes identifying users with similar preferences by comparing sets of learned user preferences and presenting content items to at least one user of the plurality of users by identifying for presentation content items selected by users identified as having learned user preferences that are similar to the learned user preferences of said at least one user.

Claims

1. A method of selecting and presenting a subset of content items to a user based on content item selections by other users having similar preferences as determined at least in part based on analyzing the weights of relative preferences and based on analyzing intervals of time occurring between user selection of similar content items, the method comprising:

providing access to a set of content items, each content item having at least one associated descriptive term to describe the content item;

receiving input entered by the user for identifying content items;

in response to the input entered by the user, presenting a corresponding subset of content items;

receiving selection actions of content items from the user who entered the input for which the subset of content items was presented;

analyzing the descriptive terms associated with the selected content items to identify sets of actions resulting in the selection of similar content items, wherein similarity is determined by comparing the descriptive terms associated with any one of the selected content items with any of the previously selected content items;

analyzing the date, day, and time of at least three of the individual selection actions of the sets of actions to learn arbitrary periodicities corresponding to a particular set of actions for selecting similar content items, wherein the arbitrary periodicities each comprise repeated similar intervals of time between user actions resulting in the selections of similar content items;

for each user of the plurality of users, learning the preferences of each user by associating a weight and arbitrary periodicity with each descriptive term associated with the selection actions of the user, said weight being indicative of the amount of use by the user of the selected content items associated with each of the descriptive terms and said arbitrary periodicity being the learned arbitrary periodicity of the descriptive terms associated with the set of similar content items, so that the user preferences are specified as a set of descriptive terms, each with associated weights and arbitrary periodicities;

identifying users with similar preferences by comparing sets of learned user preferences to determine if the sets of preferences of the users match within a specified threshold; and

presenting content items to at least one user of the plurality of users by identifying for presentation content items selected by users identified as having learned user preferences that are similar to the learned user preferences of said at least one user.

2. The method of claim 1, wherein the presented content items are ordered by promoting the ranking of relatively more popular content items, the popularity of a content item indicating a relative measure of a likelihood that the content item is desired by the user.

3. The method of claim 1, wherein the set of content items includes at least one of television program items, movie items, audio/video media items, music items, contact information items, personal schedule items, web content items, purchasable product items, and advertisements for purchasable product items.

4. The method of claim 1, wherein the set of content items includes at least one of television program items, movie items, and audio/video media items and the descriptive terms includes at least one of title, cast, director, content description, and keywords associated with the content.

5. The method of claim 1, wherein the set of content items is contained on at least one of a cable television system, a video-on-demand system, an IPTV system, and a personal video recorder.

6. The method of claim 1, wherein the input is entered by the user on an input constrained device.

7. The method of claim 6, wherein the input constrained device has a plurality of overloaded keys, each of the overloaded keys representing two or more characters.

8. The method of claim 1, wherein the input is entered by the user on at least one of a telephone, a PDA, a computer, and a remote control.

9. The method of claim 1, wherein the presented content items are presented on at least part of a television screen.

10. The method of claim 1, wherein the presented content items are presented on a display constrained device.

11. The method of claim 10, wherein the display constrained device is at least one of a telephone, a PDA, and a remote control.

12. The method of claim 1, wherein the input comprises at least one prefix of a word for describing the desired content items.

13. The method of claim 12, wherein the input comprises at least two prefixes of a phrase for describing the desired content items.

14. The method of claim 1, wherein at least one of receiving the input entered by the user, presenting the corresponding subset of content items, receiving selection actions, learning the preferences of each user, identifying users with similar preferences, and presenting content items is performed on a user client device.

15. The method of claim 1, further comprising determining a geographic location of the user at the time of at least one of the selection actions, wherein the learning the preferences of each user further including associating the geographic location with each descriptive term associated with the corresponding selection actions of the user.

16. The method of claim 1, wherein at least one of receiving input entered by the user, presenting the corresponding subset of content items, receiving selection actions, learning the preferences of each user, identifying users with similar preferences, and presenting content items is performed on a server system remote from the user.

17. A system for of selecting and presenting a subset of content items to a user based on content item selections by other users having similar preferences as determined at least in part based on analyzing the weights of relative preferences and based on analyzing intervals of time occurring between user selection of similar content items, the system comprising:

computer memory store comprising instructions in computer readable form that when executed cause a computer system to;

provide access to a set of content items, each content item having at least one associated descriptive term to describe the content item;

receive input entered by the user for identifying content items;

in response to the input entered by the user, present a corresponding subset of content items;

receive selection actions of content items from the user who entered the input for which the subset of content items was presented;

analyze the descriptive terms associated with the selected content items to identify sets of actions resulting in the selection of similar content items, wherein similarity is determined by comparing the descriptive terms associated with any one of the selected content items with any of the previously selected content items;

analyze the date, day, and time of at least three of the individual selection actions of the sets of actions to learn arbitrary periodicities corresponding to a particular set of actions for selecting similar content items, wherein the arbitrary periodicities each comprise repeated similar intervals of time between user actions resulting in the selections of similar content items;

for each user of the plurality of users, learn the preferences of each user by associating a weight and arbitrary periodicity with each descriptive term associated with the selection actions of the user, said weight being indicative of the amount of use by the user of the selected content items associated with each of the descriptive terms and said arbitrary periodicity being the learned arbitrary periodicity of the descriptive terms associated with the set of similar content items, so that the user preferences are specified as a set of descriptive terms, each with associated weights and arbitrary periodicities;

identify users with similar preferences by comparing sets of learned user preferences to determine if the sets of preferences of the users match within a specified threshold; and

present content items to at least one user of the plurality of users by identifying for presentation content items selected by users identified as having learned user preferences that are similar to the learned user preferences of said at least one user.

18. The system of claim 17, wherein the presented content items are ordered by promoting the ranking of relatively more popular content items, the popularity of a content item indicating a relative measure of a likelihood that the content item is desired by the user.

19. The system of claim 17, wherein the set of content items includes at least one of television program items, movie items, audio/video media items, music items, contact information items, personal schedule items, web content items, purchasable product items, and advertisements for purchasable product items.

20. The system of claim 17, wherein the set of content items includes at least one of television program items, movie items, and audio/video media items and the descriptive terms includes at least one of title, cast, director, content description, and keywords associated with the content.

21. The system of claim 17, wherein the set of content items is contained on at least one of a cable television system, a video-on-demand system, an IPTV system, and a personal video recorder.

22. The system of claim 17, wherein the input is entered by the user on an input constrained device.

23. The system of claim 22, wherein the input constrained device has a plurality of overloaded keys, each of the overloaded keys representing two or more characters.

24. The system of claim 17, wherein the input is entered by the user on at least one of a telephone, a PDA, a computer, and a remote control.

25. The system of claim 17, wherein the presented content items are presented on at least part of a television screen.

26. The system of claim 17, wherein the presented content items are presented on a display constrained device.

27. The system of claim 26, wherein the display constrained device is at least one of a telephone, a PDA, and a remote control.

28. The system of claim 17, wherein the input comprises at least one prefix of a word for describing the desired content items.

29. The system of claim 28, wherein the input comprises at least two prefixes of a phrase for describing the desired content items.

30. The system of claim 17, wherein at least one of receiving the input entered by the user, presenting the corresponding subset of content items, receiving selection actions, learning the preferences of each user, identifying users with similar preferences, and presenting content items is performed on a user client device.

31. The system of claim 17, the computer memory store further comprising instructions that cause the computer system to determine a geographic location of the user at the time of at least one of the selection actions, wherein the learning the preferences of each user further including associating the geographic location with each descriptive term associated with the corresponding selection actions of the user.

32. The system of claim 17, wherein at least one of receiving input entered by the user, presenting the corresponding subset of content items, receiving selection actions, learning the preferences of each user, identifying users with similar preferences, and presenting content items is performed on a server system remote from the user.