Content recommendations for social networks

• US 9,275,171 B2
• Filed: 11/22/2013
• Issued: 03/01/2016
• Est. Priority Date: 09/22/2006
• Status: Active Grant

• Current Assignee: John Nicholas and Kristin Gross Trust
• Original Assignee: John Nicholas and Kristin Gross Trust
• Inventors: Gross, John Nicholas
• Primary Examiner(s): Hoang, Son T
• Application Number:
  US14/087,406
• Publication Number:
  US 20140081965A1
• Time in Patent Office: 830 Days
• Field of Search: 707/705
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/90324 using system suggestions
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A recommendation system generates customized recommendations for social network members. The outputs for recommended content are personalized based on members'"'"' explicit and implicit interests derived from user actions, content selections, etc.

Claims

1. A method of presenting personalized recommendation results to a user at a social networking site with a computing system, comprising:

(a) processing user profile information for the user by the computing system to identify a first set of user content predicted by a recommendation engine to be liked by the user;

(b) by the computing system, identifying a second set of users who are designated by the user as friends of the user at the social networking site, and an engagement rating by the user with each user of the second set of users, wherein the engagement rating measures interaction level of the user towards each user of the second set of users in a pre-defined period of time;

(c) by the computing system, identifying actions taken by the second set of users in reviewing a second set of user content associated with the second set of users;

(d)(1) by the computing system, identifying explicit content ratings given by the second set of users for the second set of user content, and

(d)(2) by the computing system, weighing each content item of the second set of user content using both a time recency value for the content item and the engagement rating for a respective friend who rated the content item, wherein the time recency value indicates how recently the respective friend rated the content item;

(e) by the computing system, processing the first set of user content, the weighted second set of user content and the explicit content ratings using the recommendation engine to generate a list of top ranked recommended content predicted to be liked by the user; and

(f) by the computing system, presenting one or more content items from the list of top ranked recommended content within a graphical user interface for the user at the social networking site during an Internet session.

2. The method of claim 1, further including a step of:

providing an advertisement related to the list of top ranked recommended content in the graphical interface.

3. The method of claim 1, wherein the one or more content items includes a story, a post, or a multi-media item recommended for the user.

4. The method of claim 1, wherein the list of top ranked recommended content includes links to one or more websites.

5. The method of claim 1, wherein the actions by the second set of users comprise selection of at least an advertisement.

6. The method of claim 1, wherein the actions by the second set of users comprise posting of content.

7. The method of claim 1, wherein the actions by the second set of users comprise reviewing of content.

8. The method of claim 1, wherein the actions by the second set of users comprise being memberships in an online group.

9. The method of claim 1, wherein a rating for a first content item on the list of top ranked recommended content for the user is determined in part by correlating rankings given by the user and the second set of users for other particular content items in the list.

10. The method of claim 1, wherein a rating for a first content item on the list of top ranked recommended content for the user is determined in part by correlating rankings given by the user and the second set of users for other particular content sources.

11. The method of claim 10, wherein the ratings are obtained from a separate website from the social networking site.

12. The method of claim 1, wherein the engagement rating is determined by computing a frequency of interaction of the user with others of the second set of users.

13. The method of claim 1, wherein the time recency value for the content item is adjusted based on a frequency of interaction by the user with the second set of users.

14. The method of claim 1, wherein the weighting is computed based on a product of a frequency of interaction of the user with others of the second set of users and an age of the explicit content ratings.

15. The method of claim 14, where older endorsements of content by higher rated friends are scored higher than more recent endorsements of content from lower rated friends.

16. The method of claim 1, including a step of:

weighing the actions by each user of the second set of users also using the time recency value and the engagement rating for each user.

17. A method of presenting personalized recommendation results to a user at a social networking site with a computing system, comprising:

(a) monitoring, by the computing system, an interaction of the user with content at the social networking site to identify ratings given by the user for a first set of user content;

(b) processing user profile information for the user by the computing system to identify a second set of user content predicted by a recommendation engine to be liked by the user;

(c) by the computing system, identifying a second set of users who are designated by the user as friends of the user at the social networking site and an engagement rating by the user with each user of the second set of users, wherein the engagement rating measures interaction level of the user towards each user of the second set of users in a pre-defined period of time;

(d) by the computing system, identifying actions taken by the second set of users to identify a third set of user content;

wherein the identified actions include at least;

searches for content items; and

reviews of the content items; and

ratings of the content items; and

ratings of content sources associated with the content items; and

ratings of postings by users other than the second set of users;

(e)(1) by the computing system, weighing each content item of the third set of user content both by a time recency value for the content item as the engagement rating for a respective friend who rated the content item, wherein the time recency value indicates how recently the respective friend rated the content item;

(e)(2) processing the first set of user content, the second set of user content and the third set of user content to generate a list of top ranked recommended content predicted to be liked by the user with the recommendation engine; and

(f) by the computing system, presenting one or more content items from the list of top ranked recommended content within a graphical interface for the user at the social networking site during an Internet session.