Intelligent system and methods of recommending media content items based on user preferences

• US 9,854,310 B2
• Filed: 05/14/2013
• Issued: 12/26/2017
• Est. Priority Date: 12/21/1999
• Status: Expired due to Fees

• Current Assignee: TiVo Solutions Inc. (Adeia Inc.)
• Original Assignee: TiVo Solutions Inc. (Adeia Inc.)
• Inventors: Ali, Kamal, Van Stam, Wijnand J.
• Primary Examiner(s): Kumar, Pankaj
• Assistant Examiner(s): ANDRAMUNO, FRANKLIN S
• Application Number:
  US13/894,299
• Publication Number:
  US 20130254811A1
• Time in Patent Office: 1,687 Days
• Field of Search: 725 32, 725 34- 35, 725 46, 725105
• 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:

periodically receiving, by a client device over a communication network, a list of correlating items from a server, the list of correlating items created by the server using anonymous aggregated user ratings received from a plurality of client devices, the correlating items comprising at least one or more of pairs of programs and a corresponding correlation factor for each pair of programs indicating a correlation between programs in each pair of programs, a particular program appears in two or more pairs of programs;

calculating, by control circuitry at the client device, based on a collaborative filtering algorithm, prediction ratings of a first media content for a user associated with the client device, wherein the collaborative filtering algorithm uses correlation factors from the list of correlating items and previous ratings of a second media content from the user associated with the client device.

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

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

4. The method of claim 1, wherein the first media content comprises at least one of:

network television programming, cable television programming, films, pay-per-view television programming, or video.

5. The method of claim 1, wherein the list of correlated items comprises at least one item rated by the user associated with the client device and at least one item unrated by the user associated with the client device.

6. The method of claim 1, further comprising:

predicting, by the client device, a rating for an unrated media content based on a correlation in the list of correlating items.

7. A non-transitory computer-readable medium storing one or more sequences of instructions, wherein execution of the one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

periodically receiving, by a client device over a communication network, a list of correlating items from a server, the list of correlating items created by the server using anonymous aggregated user ratings received from a plurality of client devices, the correlating items comprising at least one or more of pairs of programs and a corresponding correlation factor for each pair of programs indicating a correlation between programs in each pair of programs, a particular program appears in two or more pairs of programs;

calculating, by control circuitry at the client device, based on a collaborative filtering algorithm, prediction ratings of a first media content for a user associated with the client device, wherein the collaborative filtering algorithm uses correlation factors from the list of correlating items and previous ratings of a second media content from the user associated with the client device.

8. The non-transitory computer-readable medium of claim 7, wherein the plurality of client devices comprise a plurality of video recording client devices.

9. The non-transitory computer-readable medium of claim 7, wherein the user-rated items received from a client device includes each item rated by the user of the client device and a corresponding rating assigned by the user.

10. The non-transitory computer-readable medium of claim 7, wherein the first media content comprises at least one of:

network television programming, cable television programming, films, pay-per-view television programming, or video.

11. The non-transitory computer-readable medium of claim 7, wherein the list of correlated items comprises at least one item rated by the user associated with the client device and at least one item unrated by the user associated with the client device.

12. The non-transitory computer-readable medium of claim 7, further comprising:

predicting, by the client device, a rating for an unrated media content based on a correlation in the list of correlating items.

13. An apparatus comprising:

a client device;

a correlating items receiver, at the client device, implement at least partially in hardware, that periodically receives a list of correlating items from a server over a communication network, the list of correlating items created by the server using anonymous aggregated user ratings received from a plurality of client devices, the correlating items comprising at least one or more of pairs of programs and a corresponding correlation factor for each pair of programs indicating a correlation between programs in each pair of programs, a particular program appears in two or more pairs of programs;

a ratings predictor, at the client device, implement at least partially in hardware, that calculates, based on a collaborative filtering algorithm, prediction ratings of a first media content for a user associated with the client device, wherein the collaborative filtering algorithm uses correlation factors from the list of correlating items and previous ratings of a second media content from the user associated with the client device.

14. The apparatus of claim 13, wherein the plurality of client devices comprise a plurality of video recording client devices.

15. The apparatus of claim 13, wherein the user-rated items received from a client device includes each item rated by the user of the client device and a corresponding rating assigned by the user.

16. The apparatus of claim 13, wherein the first media content comprises at least one of:

network television programming, cable television programming, films, pay-per-view television programming, or video.

17. The apparatus of claim 13, wherein the list of correlated items comprises at least one item rated by the user associated with the client device and at least one item unrated by the user associated with the client device.

18. The apparatus of claim 13, wherein the ratings predictor predicts a rating for an unrated media content based on a correlation in the list of correlating items.