System Apparatus Circuit Method and Associated Computer Executable Code for Generating and Providing Content Recommendations to a Group of Users

• US 20130238710A1
• Filed: 04/28/2013
• Published: 09/12/2013
• Est. Priority Date: 08/18/2010
• Status: Active Grant

• Current Assignee: Jinni Media Limited
• Original Assignee: Jinni Media Limited
• Inventors: Meshulam, Ram, Rimon, Mordechai Mori, Ben Zaken, Izhak
• Granted Patent: US 9,792,640 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 30/0605 Supply or demand aggregation
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Disclosed are systems, apparatuses, circuits, methods and computer executable code sets for generating and providing content recommendations to match the tastes and preferences of a group of users. a Recommendation Engine is used for generating two or more individual content recommendation sets for each of the members in the user group. A Recommendation Aggregation Module is used for adding and combining the individual content recommendation sets into an aggregated recommendation set. a Recommendation Selection Module is used for selecting at least a subset of the content items in the aggregated recommendation set for inclusion in a content recommendation result set. A Profile Engine is used for building individual group users profiles from which a merged group profile is constructed, or for building a single joint group profile based on inputs from multiple group users.

Claims

1. A system for generating and providing content recommendations to match the tastes and preferences of a group of users comprising:

digital processing circuitry; and

digital memory including digital processor executable code adapted to provide;

a Recommendation Engine for generating two or more individual content recommendation sets for each of the members in the user group;

a Recommendation Aggregation Module for adding and combining the individual content recommendation sets into an aggregated recommendation set; and

a Recommendation Selection Module for selecting at least a subset of the content items in the aggregated recommendation set for inclusion in a content recommendation result set.

2. The system according to claim 1 wherein said Recommendation Selection Module further comprises a Per Item User Counter for recording and counting the number of users to which a given content item was recommended.

3. The system according to claim 1 wherein said Recommendation Selection Module further comprises an Item-User Relevancy Level Calculator for estimating how relevant is a given content item to at least some of the members in the user group.

4. The system according to claim 1 wherein said Recommendation Selection Module further comprises a Previous Items Similarity Calculator for referencing a Previous Recommendations Log database and estimating the similarity level of a given content item to content items selected for the same, and/or a partially overlapping, group(s) in previous rounds of selection.

5. The system according to claim 1 wherein said Recommendation Selection Module further comprises a Direct User Input Analyzer for processing and considering explicit and/or implicit inputs, of members in the user group, relating to a given content item.

6. The system according to claim 1 wherein said Recommendation Selection Module further comprises a Community Ratings Analyzer for referencing a proprietary and/or third party Community Ratings Storage database and giving a higher preference for inclusion in the content recommendation result set to content items having higher popularity in the community.

7. The system according to claim 1 wherein said Recommendation Selection Module is further adapted to provide the selected content recommendation result set with a per-user content match and confidence rates feedback for content items in the selected result set.

8. The system according to claim 1 wherein said Recommendation Engine is functionally associated with a Content Catalogs Prioritization Module for offering content items from specific preferred catalogues.

9. A system for generating and providing content recommendations to match the tastes and preferences of a group of users comprising:

digital processing circuitry; and

digital memory including digital processor executable code adapted to provide;

a Profile Engine for generating user taste profiles; and

a Recommendation Engine for generating content recommendations based on the generated profiles.

10. The system according to claim 9 further comprising a Profile Aggregation Module, wherein:

said Profile Engine generates an individual profile for each member in the group of users;

said Profile Aggregation Module adds and combines the individual user profiles into a merged group profile; and

said Recommendation Engine generates a content recommendation set for the user group based on the merged group profile.

11. The system according to claim 9 further comprising a Recommendation Selection Module, wherein:

said Profile Engine generates a single joint users'"'"' taste profile based on inputs from multiple members of the group of users;

said Recommendation Engine generates a content recommendation set for the user group based on the joint users'"'"' taste profile; and

said Recommendation Selection Module selects at least a subset of the content items in the joint profile content recommendation set for inclusion in a content recommendation result set.

12. A method for generating and providing content recommendations to match the tastes and preferences of a group of users comprising:

generating two or more individual content recommendation sets for each of the members in the user group;

adding and combining the individual content recommendation sets into an aggregated recommendation set; and

selecting at least a subset of the content items in the aggregated recommendation set for inclusion in a content recommendation result set.

13. The method according to claim 12 wherein selecting at least a subset of the content items further comprises recording and counting the number of users to which a given content item was recommended.

14. The method according to claim 12 wherein selecting at least a subset of the content items further comprises estimating how relevant is a given content item to at least some of the members in the user group.

15. The method according to claim 12 wherein selecting at least a subset of the content items further comprises:

referencing a Previous Recommendations Log database; and

estimating the similarity level of a given content item to content items selected for the same group in previous rounds of selection.

16. The method according to claim 12 wherein selecting at least a subset of the content items further comprises processing and considering explicit and/or implicit inputs of members in the user group relating to a given content item.

17. The method according to claim 12 wherein selecting at least a subset of the content items further comprises referencing a Community Ratings Storage database and giving a higher preference for inclusion in the content recommendation result set to content items having higher popularity in the community.

18. The method according to claim 12 wherein selecting at least a subset of the content items in the aggregated recommendation set further comprises providing the selected content recommendation result set with a per-user content match and confidence rates feedback.

19. The method according to claim 12 wherein generating two or more individual content recommendation sets for each of the members in the user group further comprises offering content items from specific preferred catalogues.

20. A method for generating and providing content recommendations to match the tastes and preferences of a group of users comprising:

generating user taste profiles; and

generating content recommendations based on the generated profiles.

21. The method according to claim 20, wherein:

generating user taste profiles comprises generating an individual profile for each member in the group of users and adding and combining the individual user profiles into a merged group profile; and

generating content recommendations is based on the merged group profile.

22. The method according to claim 20 wherein:

generating user taste profiles comprises generating a single joint users'"'"' taste profile based on inputs from multiple members of the group of users;

generating content recommendations is based on the joint users'"'"' taste profile; and

further comprising selecting at least a subset of the content items in the joint profile content recommendation set for inclusion in a content recommendation result set.