System and method for providing personal content recommendations

• US 9,654,721 B2
• Filed: 07/10/2007
• Issued: 05/16/2017
• Est. Priority Date: 07/10/2007
• Status: Active Grant

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: Raghavan, Senthil K., Patel, Laxmi M., Mehta, Japan A., Ruiz-Velasco, Enrique
• Primary Examiner(s): Lewis, Jonathan V
• Application Number:
  US11/775,643
• Publication Number:
  US 20090019488A1
• Time in Patent Office: 3,598 Days
• Field of Search: 725 46
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04892 Arrangements for controlling cursor position based on codes indicative of cursor displacements from one discrete location to another, e.g. using cursor control keys associated to different directions or using the tab key arrangements for controlling cursor position based on coordinate signals G06F3/038
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; 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 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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 21/4622 Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/47 End-user applications
• 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 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A system and method for providing personal content recommendations comprising a receiver to receive and collect one or more user commands at one or more modules, a processor to generate, at the one or more modules, one or more personalized recommendations based on the one or more user commands, and a transmitter to transmit the one or more personalized recommendations to be displayed at a display device in response to receiving one or more user inputs to display the one or more personalized recommendations.

Claims

1. A method, comprising:

receiving, at a first receiver of a computing device, one or more user commands;

transmitting, at a transmitter of the computing device, to at least one module, the one or more user commands to generate one or more personalized recommendations based on the one or more user commands, wherein generating the one or more personalized recommendations comprises taking into account the user'"'"'s implicit preferences and matching one or more programs selected by a user with one or more similar programs selected by one or more other users within a community, wherein the community comprises users over multiple geographic localities who subscribe to a personalized recommendations feature, wherein the user'"'"'s implicit preferences are quantified as an implicit rating for the user based on raw data and discovered data, and wherein the discovered data is determined based on raw data;

receiving, at a second receiver of the computing device, the one or more personalized recommendations; and

outputting, at a display device, the one or more personalized recommendations in an electronic program guide (EPG) in response to receiving one or more user inputs to display the one or more personalized recommendations to the user, the EPG listing programs chronologically and sequentially by channel, a first portion of the listed programs in the EPG comprising the one or more personalized recommendations along with the quantified implicit rating for the user for the one or more personalized recommendations and a second portion of the listed programs in the EPG comprising no personalized recommendations for the second portion of listed programs.

2. The method of claim 1, wherein generating the one or more personalized recommendations comprises matching a profile of the user with one or more profiles of similar users within the community.

3. The method of claim 1, wherein the matching is based on matching metadata content in the one or more programs with the one or more similar programs.

4. The method of claim 1, wherein the matching is based on matching ratings provided by the user for the one or more programs with ratings provided by additional users for the one or more similar programs.

5. The method of claim 1, wherein the matching is based on matching relationships provided by a third party.

6. The method of claim 1, wherein generating the one or more personalized recommendations is based on recommendations from at least one of a host service provider, a third party, and a community poll.

7. The method of claim 1, wherein generating the one or more personalized recommendations comprises matching based on at least one of a profile of the user with one or more profiles of similar users within the community and additional relationships provided by at least one of a host service provider, a third party, and a community poll.

8. The method of claim 1, wherein the one or more personalized recommendations comprise recommendations for at least one of broadcast programming, video on demand programming, per-per-view programming, advertisements, Internet video, Internet music, and merchandise.

9. The method of claim 1, further comprising tagging at least one or more of the personalized recommendations for personalized display customization.

10. A computer readable media comprising code to perform the acts of the method of claim 1.

11. The method of claim 1, wherein the user'"'"'s implicit preferences comprise preferences inferred from a user'"'"'s commands to change a channel or program to another, how long the user viewed the channel or program before changing, the date and time the change command was received or performed, the name of the program changed to or from, the type of program changed to or from, or the channel changed to or from.

12. The method of claim 1, wherein the quantified implicit rating for the user for the one or more personalized recommendations displayed in the EPG are displayed using a color or a percentage indicator, wherein the color or the percentage indicator represent a degree of personalized recommendation to the user.

13. A system, comprising:

a first receiver to receive one or more user commands;

a transmitter to transmit, to at least one module, the one or more user commands to generate one or more personalized recommendations based on the one or more user commands, wherein generating the one or more personalized recommendations comprises taking into account the user'"'"'s implicit preferences and matching one or more programs selected by a user with one or more similar programs selected by one or more other users within a community, wherein the community comprises users over multiple geographic localities who subscribe to a personalized recommendations feature, wherein the user'"'"'s implicit preferences are quantified as an implicit rating for the user based on raw data and discovered data, and wherein the discovered data is determined based on raw data;

a second receiver to receive the one or more personalized recommendations; and

an output configured to display, at a display device, the one or more personalized recommendations in an electronic program guide (EPG) in response to receiving one or more user inputs to display the one or more personalized recommendations, the EPG listing programs chronologically and sequentially by channel, a first portion of the listed programs in the EPG comprising the one or more personalized recommendations along with the quantified implicit rating for the user for the one or more personalized recommendations and a second portion of the listed programs in the EPG comprising no personalized recommendations for the second portion of listed programs.

14. A method, comprising:

receiving, by a receiver at one or more modules of a computing device, one or more user commands;

generating, by a processor at the one or more modules, one or more personalized recommendations based on the one or more user commands, wherein generating the one or more personalized recommendations comprises taking into account the user'"'"'s implicit preferences and matching one or more programs based on the one or more user commands with one or more similar programs selected by one or more other users within a community, wherein the community comprises users over multiple geographic localities who subscribe to a personalized recommendations feature, wherein the user'"'"'s implicit preferences are quantified as an implicit rating for the user based on raw data and discovered data, and wherein the discovered data is determined based on raw data; and

transmitting, at a transmitter at the one or more modules, the one or more personalized recommendations to be displayed at a display device in an electronic program guide (EPG) in response to receiving one or more user inputs to display the one or more personalized recommendations, the EPG listing programs chronologically and sequentially by channel, a first portion of the listed programs in the EPG comprising the one or more personalized recommendations along with the quantified implicit rating for the user for the one or more personalized recommendations and a second portion of the listed programs in the EPG comprising no personalized recommendations for the second portion of listed programs.

15. The method of claim 14, wherein generating the one or more personalized recommendations comprises matching a profile of the user with one or more profiles of similar users within the community.

16. The method of claim 14, wherein the matching is based on matching metadata content in the one or more programs with the one or more similar programs.

17. The method of claim 14, wherein the matching is based on matching ratings provided by the user for the one or more programs with ratings provided by additional users for the one or more similar programs.

18. The method of claim 14, wherein the matching is based on matching relationships provided by a third party.

19. The method of claim 14, wherein generating the one or more personalized recommendations is based on recommendations from at least one of a host service provider, a third party, and a community poll.

20. The method of claim 14, wherein generating the one or more personalized recommendations comprises matching based on at least one of a profile of the user with one or more profiles of similar users within the community and additional relationships provided by at least one of a host service provider, a third party, and a community poll.

21. The method of claim 14, wherein the one or more personalized recommendations comprise recommendations for at least one of broadcast programming, video on demand programming, pay-per-view programming, advertisements, Internet video, Internet music, and merchandise.

22. The method of claim 14, further comprising tagging at least one or more of the personalized recommendations for personalized display customization.

23. A computer readable media comprising code to perform the acts of the method of claim 14.

24. A system, comprising:

a receiver to receive and collect one or more user commands at one or more modules;

a processor to generate, at the one or more modules, one or more personalized recommendations based on the one or more user commands, wherein generating the one or more personalized recommendations comprises taking into account the user'"'"'s implicit preferences and matching one or more programs based on the one or more user commands with one or more similar programs selected by one or more other users within a community, wherein the community comprises users over multiple geographic localities who subscribe to a personalized recommendations feature, wherein the user'"'"'s implicit preferences are quantified as an implicit rating for the user based on raw data and discovered data, and wherein the discovered data is determined based on raw data; and

a transmitter to transmit the one or more personalized recommendations to be displayed at a display device in an electronic program guide (EPG) in response to receiving one or more user inputs to display the one or more personalized recommendations, the EPG listing programs chronologically and sequentially by channel, a first portion of the listed programs in the EPG comprising the one or more personalized recommendations along with the quantified implicit rating for the user for the one or more personalized recommendations and a second portion of the listed programs in the EPG comprising no personalized recommendations for the second portion of listed programs.