METHODS AND SYSTEMS FOR SEGMENTING RELATIVE USER PREFERENCES INTO FINE-GRAIN AND COARSE-GRAIN COLLECTIONS

• US 20070276859A1
• Filed: 03/06/2007
• Published: 11/29/2007
• Est. Priority Date: 03/06/2006
• Status: Active Grant

• Current Assignee: Veveo LLC (Xperi Inc.)
• Original Assignee: Veveo, Inc. (Adeia Inc.)
• Inventors: RAJASEKHARAN, Ajit, ARAVAMUDAN, Murali, GUPTA, Mayank, RAMAKRISHNAN, Kajamalai
• Granted Patent: US 7,529,741 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 segmenting relative user preferences into fine-grain and coarse-grain collections is provided. The method includes providing a set of content items having associated descriptive terms. The method also includes receiving user search input and, in response thereto, presenting a subset of content items. The method includes receiving user selection actions and analyzing the selections to learn the user'"'"'s preferred descriptive terms. The method includes expressing the learned preferred descriptive terms as a segmented probability distribution function having at least one fine grain segment and at least one coarse grain segment. In response to subsequent search input, the method calls for selecting and ordering a collection of content items by promoting the ranking of content items associated with the learned preferred descriptive terms of the user according to the differentiation provided by the segmented probability distribution function.

Claims

1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learned user preferences, the method comprising:

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

receiving incremental input entered by the user for incrementally identifying desired content items;

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

receiving selection actions of content items of the subset from the user;

analyzing the descriptive terms associated with the selected content items to learn the preferred descriptive terms of the user;

expressing the learned preferred descriptive terms as a segmented probability distribution function having at least one fine grain segment and at least one coarse grain segment, wherein the fine grain segment has fine grain differentiation of probability weights associated with preferred descriptive terms within the segment, and wherein the coarse grain segment has relatively coarse grain differentiation of probability weights associated with preferred descriptive terms within the segment; and

in response to receiving subsequent incremental input entered by the user, selecting and ordering a collection of content items by promoting the ranking of content items associated with the learned preferred descriptive terms of the user according to the differentiation provided by the segmented probability distribution function.

2. The method of claim 1, wherein the probability weights of the segmented probability distribution function associated with preferred descriptive terms are based on the frequency of selection of content items associated with said preferred descriptive terms.

3. The method of claim 1, wherein the probability weights of the segmented probability distribution function associated with preferred descriptive terms are based on the recency of selection of content items associated with said preferred descriptive terms.

4. The method of claim 1, wherein the probability weights of the segmented probability distribution function associated with preferred descriptive terms are based on the number of selections of content items associated with said preferred descriptive terms.

5. The method of claim 1, the segmented probability distribution function further having an overflow segment, wherein the probabilities weights within the overflow segment are not differentiated from other probabilities weights within the overflow segment, and wherein probabilities weights within the overflow segment are differentiated from the probability weights within the coarse and fine grain segments.

6. The method of claim 1, wherein the coarse grain segment includes at least two weight groups, each weight group having a preselected range of probability weight values that determine which probability weights are in the weight group so that any probability weights in a particular weight group are not differentiated from other probability weights in the same group, and wherein the probabilities weights in different weight groups are differentiated from each other.

7. The method of claim 6, wherein each weight group includes a high probability weight value and a low probability weight value defining the preselected range of probability weight values.

8. The method of claim 7, wherein the high probability weight value and the low probability weight value are separated by at least one order of magnitude.

9. The method of claim 1, wherein the probability weights associated with preferred descriptive terms are integer values.

10. The method of claim 1, wherein the learned preferred descriptive terms are further based on:

analyzing the date, day, and time of the user selection actions and analyzing the descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the descriptive terms of the selected content item with the previously selected content item, and wherein the periodicity indicates the amount of time between user selections of similar content items relative to a reference point; and

associating the learned periodicity with descriptive terms associated with the similar content items;

wherein the selecting and ordering the collection of content items is further based on promoting the ranking of those content items associated with descriptive terms further associated with periodicities similar to the date, day, and time of the subsequent incremental input.

11. The method of claim 1, wherein the selecting and ordering the collection of content items is further based on popularity values associated with the content items, each popularity value indicating a relative measure of a likelihood that the corresponding content item is desired by the user.

12. 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, and purchasable product items.

13. 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.

14. 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.

15. The method of claim 1, wherein at least one of the incremental input and the subsequent incremental input are entered by the user on an input constrained device.

16. The method of claim 15, wherein the input constrained device has a plurality of overloaded keys, each of the overloaded keys representing two or more characters.

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

18. The method of claim 1, further comprising presenting the ordered collection of content items on at least part of a television screen.

19. The method of claim 1, further comprising presenting the ordered collection of content items on a display constrained device.

20. The method of claim 19, wherein the display constrained device is at least one of a telephone, a PDA, and a remote control.

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

22. The method of claim 21, wherein at least one of the incremental input and the subsequent incremental input comprises at least two prefixes of a phrase for describing the desired content items.

23. The method of claim 1, wherein at least one of receiving incremental input, presenting the subset of content items, receiving selection actions, analyzing the descriptive terms, expressing the learned preferred descriptive terms as a segmented probability distribution function, and selecting and ordering the collection of content items is performed on a server system remote from the user.

24. The method of claim 1, wherein at least one of receiving incremental input, presenting the subset of content items, receiving selection actions, analyzing the descriptive terms, expressing the learned preferred descriptive terms as a segmented probability distribution function, and selecting and ordering the collection of content items is performed on a user client device.

25. The method of claim 1, wherein the segmented probability distribution is stored on a user client device and selecting and ordering the collection of content items includes selecting and ordering content items stored on the client device.

26. The method of claim 1, further comprising:

organizing the content items of the set of content items into groupings based on the informational content of the content items;

determining a context in which the user performed the selection actions, the context including at least one of geographic location of the user, day, date, time, and the group into which the selected content items are organized; and

associating the contexts of the user selection actions with the preferred descriptive terms learned from the corresponding user selections;

wherein only preferred descriptive terms associated with the context in which the user entered the subsequent incremental input are used in the selecting and ordering of the collection of content items.