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

Patent metadata

Field	Value
Publication number	US-9854310-B2
Application number	US-201313894299-A
Country	US
Kind code	B2
Filing date	May 14, 2013
Priority date	Dec 21, 1999
Publication date	Dec 26, 2017
Grant date	Dec 26, 2017

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.

First claim

What is claimed is:

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.

Assignees

• Tivo Solutions Inc

Inventors

• Ali Kamal
• Van Stam Wijnand J

Classifications

• G06F16/7867

  using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings · CPC title

• H04N21/4532

  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 title

• H04N21/47214

  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 title

• H04N21/443

  OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB · CPC title

• H04N7/17318

  Direct or substantially direct transmission and handling of requests · CPC title