Interestingness recommendations in a computing advice facility

• US 8,972,314 B2
• Filed: 03/03/2014
• Issued: 03/03/2015
• Est. Priority Date: 11/02/2007
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Pinckney, Thomas, Dixon, Christopher, Gattis, Matthew Ryan
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US14/195,479
• Publication Number:
  US 20140180979A1
• Time in Patent Office: 365 Days
• Field of Search: 706/12
• US Class Current: 706/12
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

The present disclosure provides a recommendation to a user through a computer-based advice facility, comprising collecting topical information, wherein the collected topical information includes an interestingness aspect; filtering the collected topical information based on the interestingness aspect; determining an interestingness rating from the collected topical information, wherein the determining is through the computer-based advice facility; and providing a user with the recommendation related to the topical information based on the interestingness rating.

Claims

1. A computer-implemented method comprising:

detecting an acceleration in social activity associated with a content item posted on a website;

determining, based on the detected acceleration in social activity, that the content item is relevant to a particular user; and

providing, using one or more hardware processors, a recommendation of the content item to the particular user, based on the determining that the content item is relevant to the particular user.

2. The method of claim 1, wherein the website is associated with an online social network.

3. The method of claim 1, wherein the social activity is posted by a second user that is a connected to the particular user on an online social network.

4. The method of claim 1, wherein the social activity is posted by a second user determined to be an influential user of an online social network.

5. The method of claim 1, wherein the social activity is posted by a second user determined to have similar tastes to the particular user.

6. The method of claim 1, wherein the social activity is saving a recommendation for the content item.

7. The method of claim 1, wherein the social activity is at least one of commenting and discussing the content item.

8. The method of claim 1, wherein the social activity is a rating of the content item.

9. The method of claim 1, wherein the social activity describes a purchase of at least one of a product and a service.

10. The method of claim 1, wherein the content item describes a location, and the social activity posted on the website is a check-in at the location.

11. The method of claim 1, wherein the particular content item is a recommendation for at least one of a product and a service.

12. The method of claim 1, wherein the particular content item is an advertisement.

13. The method of claim 1, wherein the particular content item is a review of a product or service.

14. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

detecting an acceleration in social activity associated with a content item posted on a website;

determining, based on the detected acceleration in social activity, that the content item is relevant to a particular user; and

providing a recommendation of the content item to the particular user, based on the determining that the content item is relevant to the particular user.

15. The storage medium of claim 14, wherein the social activity is saving a recommendation for the content item.

16. The storage medium of claim 14, wherein the social activity is at least one of commenting and discussing the content item.

17. The storage medium of claim 14, wherein the content item describes a location, and the social activity posted on the website is a check-in at the location.

18. The storage medium of claim 14, wherein the particular content item is a recommendation for at least one of a product and a service.

19. The storage medium of claim 14, wherein the particular content item is an advertisement.

20. A system comprising:

a recommendation module, comprising one or more hardware processors, configured to;

detect an acceleration in social activity associated with a content item posted on a website;

determine, based on the detected acceleration in social activity, that the content item is relevant to a particular user; and

provide a recommendation of the content item to the particular user, based on the determining that the content item is relevant to the particular user.