INTELLIGENT SYSTEM AND METHODS OF RECOMMENDING MEDIA CONTENT ITEMS BASED ON USER PREFERENCES

• US 20110061076A1
• Filed: 11/15/2010
• Published: 03/10/2011
• Est. Priority Date: 12/21/1999
• Status: Active Grant

• Current Assignee: TiVo Solutions Inc. (Adeia Inc.)
• Original Assignee: TiVo Inc. (Adeia Inc.)
• Inventors: Stam, Wijnand Van, Ali, Kamal
• Granted Patent: US 8,443,389 B2
• Time in Patent Office: Days
• US Class Current: 725/46
• CPC Class Codes: G06F 16/735 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/7867 using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
• CPC Class Codes: G11B 2220/20 Disc-shaped record carriers
• CPC Class Codes: G11B 27/105 of operating discs
• CPC Class Codes: G11B 27/34 Indicating arrangements indicating means incorporated in magazine or cassette G11B23/046 and G11B23/0875; indicating measured values in general G01D
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/4147 PVR [Personal Video Recorder] H04N5/76 takes precedence ; arrangements for broadcast with accumulation-type receivers H04H20/40
• CPC Class Codes: H04N 21/4316 for displaying supplemental content in a region of the screen, e.g. an advertisement in a separate window
• CPC Class Codes: H04N 21/4334 Recording operations recording of a television signal H04N5/76; arrangements for recording or accumulating broadcast information or broadcast-related information H04H60/27
• 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 21/443 OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
• 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 21/454 Content or additional data filtering, e.g. blocking advertisements
• 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 21/4663 involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/47 End-user applications
• CPC Class Codes: H04N 21/47214 for content reservation or setting reminders; for requesting event notification, e.g. of sport results or stock market stock exchange data over packet-switching network H04L12/1804; push services over packet-switching network H04L12/1859; notification of incoming messages in packet switching networks H04L51/224
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/478 Supplemental services, e.g. displaying phone caller identification, shopping application
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 21/4828 : for searching program descriptors retrieval of video data G06F16/739
• CPC Class Codes: H04N 21/485 : End-user interface for client configuration
• CPC Class Codes: H04N 21/6543 : for forcing some client operations, e.g. recording remote booting in general G06F9/4416
• CPC Class Codes: H04N 21/812 : involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 21/8166 : involving executable data, e.g. software arrangements for executing specific programs G06F9/44; broadcasting computer programmes in broadcast systems H04H20/91; involving the movement of software or configuration parameters H04L67/34
• CPC Class Codes: H04N 5/76 : Television signal recording
• CPC Class Codes: H04N 5/782 : on tape
• CPC Class Codes: H04N 7/173 : with two-way working, e.g. subscriber sending a programme selection signal
• CPC Class Codes: H04N 7/17318 : Direct or substantially direct transmission and handling of requests
• CPC Class Codes: H04N 7/17354 : in an intermediate station common to a plurality of user terminals

Abstract

A system and method for making program recommendations to users of a network-based video recording system utilizes expressed preferences as inputs to collaborative filtering and Bayesian predictive algorithms to rate television programs using a graphical rating system. The predictive algorithms are adaptive, improving in accuracy as more programs are rated.

Claims

1. A method comprising:

at a server in communication with a plurality of clients over a network connection, periodically receiving a list of user-rated items from each client of the plurality of clients, the user-rated items being associated with media content, the user-rated items in a list of user-rated items from a client of the plurality of clients being rated by a user of the client, the user-rated items in lists of user-rated items from other clients of the plurality of clients being rated by other users of the other clients, the lists of user-rated items stored in a manner that is anonymous with respect to the user and the other users, and the lists of user-rated items aggregated into an aggregated list of items;

for each unique pair of items of a plurality of unique pairs of items from the aggregated list of items, storing, by the server, a pair of user-ratings for the unique pair of items in a pair matrix;

for each pair matrix of a plurality of pair matrices, computing, by the server, a rating correlation between items of the unique pair of items;

compiling, by the server, a list of correlating items using the rating correlations; and

periodically sending, from the server to the client, the list of correlating items.

2. The method of claim 1, wherein the plurality of clients comprise a plurality of video recording clients.

3. The method of claim 1, wherein the user-rated items received from the client includes each item rated by the user of the client and the corresponding rating assigned by the user.

4. The method of claim 3, wherein the corresponding rating assigned by the user is assigned according to a scalar system of discrete ratings.

5. The method of claim 1, wherein a particular pair of user-ratings for a particular unique pair of items is stored in a particular pair matrix by:

retrieving the particular pair of user-ratings for the particular unique pair of items;

determining whether the particular pair matrix storing user-ratings for the particular unique pair of items exists;

creating the particular pair matrix for the pair of items if the particular pair matrix does not exist;

locating a cell that stores the particular pair of user-ratings in the particular pair matrix by using the particular pair of user-ratings as an index to the particular pair matrix; and

incrementing a value of the cell that corresponds to the particular pair of user-ratings.

6. The method of claim 1, further comprising filtering user-rated items from the aggregated list of items based on frequency by monitoring frequency of the user-rated items and discarding items with corresponding user-ratings that do not satisfy a threshold frequency.

7. The method of claim 1, further comprising filtering out non-significant rating correlations of unique pairs of items.

8. The method of claim 1, wherein the media content comprises any of network television programming, cable television programming, films, pay-per-view television programming, and video.

9. The method of claim 1, wherein the list of correlated items comprises at least one item rated by the user and at least one item unrated by the user.

10. One or more non-transitory computer-readable storage media storing instructions, which instructions, when executed by one or more processors, cause the one or more processors to perform:

at a server in communication with a plurality of clients over a network connection, periodically receiving a list of user-rated items from each client of the plurality of clients, the user-rated items being associated with media content, the user-rated items in a list of user-rated items from a client of the plurality of clients being rated by a user of the client, the user-rated items in lists of user-rated items from other clients of the plurality of clients being rated by other users of the other clients, the lists of user-rated items stored in a manner that is anonymous with respect to the user and the other users, and the lists of user-rated items aggregated into an aggregated list of items;

for each unique pair of items of a plurality of unique pairs of items from the aggregated list of items, storing, by the server, a pair of user-ratings for the unique pair of items in a pair matrix;

for each pair matrix of a plurality of pair matrices, computing, by the server, a rating correlation between items of the unique pair of items;

compiling, by the server, a list of correlating items using the rating correlations; and

periodically sending, from the server to the client, the list of correlating items.

11. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the plurality of clients comprise a plurality of video recording clients.

12. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the user-rated items received from the client include each item rated by the user of the client and the corresponding rating assigned by the user.

13. One or more non-transitory computer-readable storage media as recited in claim 12, wherein the corresponding rating assigned by the user is assigned according to a scalar system of discrete ratings.

14. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform storing a particular pair of user-ratings for a particular unique pair of items in a particular pair matrix by:

retrieving the particular pair of user-ratings for the particular unique pair of items;

determining whether the particular pair matrix storing user-ratings for the particular unique pair of items exists;

creating the particular pair matrix for the pair of items if the particular pair matrix does not exist;

locating a cell that stores the particular pair of user-ratings in the particular pair matrix by using the particular pair of user-ratings as an index to the particular pair matrix; and

incrementing a value of the cell that corresponds to the particular pair of user-ratings.

15. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform filtering user-rated items from the aggregated list of items based on frequency by monitoring frequency of the user-rated items and discarding items with corresponding user-ratings that do not satisfy a threshold frequency.

16. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform filtering out non-significant rating correlations of unique pairs of items.

17. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the media content comprises any of network television programming, cable television programming, films, pay-per-view television programming, and video.

18. One or more non-transitory computer-readable storage media as recited in claim 10, wherein the list of correlated items comprises at least one item rated by the user and at least one item unrated by the user.

19. A server apparatus comprising:

a network connection mechanism coupled to server receiving logic and server sending logic;

the server receiving logic configured to periodically receive, at a server in communication with a plurality of clients over a network connection, a list of user-rated items from each client of the plurality of clients, the user-rated items being associated with media content, the user-rated items in a list of user-rated items from a client of the plurality of clients being rated by a user of the client, the user-rated items in lists of user-rated items from other clients of the plurality of clients being rated by other users of the other clients, the lists of user-rated items stored in a manner that is anonymous with respect to the user and the other users, and the lists of user-rated items aggregated into an aggregated list of items;

server storage logic configured to store, by the server, for each unique pair of items of a plurality of unique pairs of items from the aggregated list of items, a pair of user-ratings for the unique pair of items in a pair matrix;

a server engine configured to;

compute, by the server, for each pair matrix of a plurality of pair matrices, a rating correlation between items of the unique pair of items; and

compile, by the server, a list of correlating items using the rating correlations;

the server sending logic configured to periodically send, from the server to the client, the list of correlating items.

20. The server apparatus of claim 19, wherein the plurality of clients comprise a plurality of video recording clients.

21. The server apparatus of claim 19, wherein the user-rated items received from the client include each item rated by the user of the client and the corresponding rating assigned by the user.

22. The server apparatus of claim 21, wherein the corresponding rating assigned by the user is assigned according to a scalar system of discrete ratings.

23. The server apparatus of claim 19, wherein the server storage logic is configured to store a particular pair of user-ratings for a particular unique pair of items in a particular pair matrix by:

retrieving the particular pair of user-ratings for the particular unique pair of items;

determining whether the particular pair matrix storing user-ratings for the particular unique pair of items exists;

creating the particular pair matrix for the pair of items if the particular pair matrix does not exist;

locating a cell that stores the particular pair of user-ratings in the particular pair matrix by using the particular pair of user-ratings as an index to the particular pair matrix; and

incrementing a value of the cell that corresponds to the particular pair of user-ratings.

24. The server apparatus of claim 19, further comprising server filtering logic configured to filter user-rated items from the aggregated list of items based on frequency by monitoring frequency of the user-rated items and discarding items with corresponding user-ratings that do not satisfy a threshold frequency.

25. The server apparatus of claim 19, further comprising server filtering logic configured to filter out non-significant rating correlations of unique pairs of items.

26. The server apparatus of claim 19, wherein the media content comprises any of network television programming, cable television programming, films, pay-per-view television programming, and video.

27. The server apparatus of claim 19, wherein the list of correlated items comprises at least one item rated by the user and at least one item unrated by the user.