Recommendations Based Upon Explicit User Similarity

• US 20140279202A1
• Filed: 03/12/2014
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Transform Sr Brands, LLC (ESL Investments, Inc.)
• Original Assignee: Sears Brands LLC (Sears Holdings Corporation)
• Inventors: Egozi, Ofer, Moran, Amit, Shamir, Oren
• Granted Patent: US 9,972,042 B2
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A system and method for providing recommendations to individuals on a social network, in which the recommendations include information indicating the similarity of the individuals to one another, to aid the individuals in judging the degree to which the opinions of the others are applicable to the themselves.

Claims

1. A method of producing a recommendation for a first user using interaction information for each of the first user and a second user of a plurality of users of a computer network, the interaction information for the first user and the second user derived from interactions of the first user and the second user with the computer network, the method comprising:

characterizing the first user and the second user with regard to each of a plurality of attributes, based upon the interaction information for the first user and the second user;

calculating a level of similarity of the first user and the second user, using the characterization of the first user and the characterization of the second user with regard to the plurality of attributes; and

presenting to the first user a recommendation of a product or a service, based upon the similarity level and the interaction information for the first user and the second user.

2. The methods according to claim 1, wherein the computer network comprises an e-commerce system.

3. The method according to claim 1, wherein the interaction information is derived from social network activities of the first user and the second user over the computer network.

4. The method according to claim 1, wherein the interaction information is derived from implicit interactions.

5. The method according to claim 4, wherein implicit interactions comprise one or more of:

a page view, a quick view, a vote on a online survey, an addition of an item to an online shopping cart, a purchase of one or more items, and/or an addition of an item to a private catalog.

6. The method according to claim 1, wherein calculating the level of similarity of the first user and the second user comprises calculating an aggregated similarity level based on user interests and product preferences.

7. The method according to claim 1, wherein calculating the level of similarity of the first user and the second user comprises correcting the plurality of weights for each of the first user and the second user based upon the number of distinct users associated with each attribute.

8. A non-transitory computer-readable medium having stored thereon a plurality of code sections, each code section comprising a plurality of instructions executable by a processor to cause the processor to perform a method of producing a recommendation for a first user using interaction information for each of the first user and a second user of a plurality of users of a computer network, the interaction information for the first user and the second user derived from interactions of the first user and the second user with the computer network, the method comprising:

characterizing the first user and the second user with regard to each of a plurality of attributes, based upon the interaction information for the first user and the second user;

calculating a level of similarity of the first user and the second user, using the characterization of the first user and the characterization of the second user with regard to the plurality of attributes; and

presenting to the first user a recommendation of a product or a service, based upon the similarity level and the interaction information for the first user and the second user.

9. The non-transitory computer-readable medium according to claim 8, wherein the computer network comprises an e-commerce system.

10. The non-transitory computer-readable medium according to claim 8, wherein the interaction information is derived from social network activities of the first user and the second user over the computer network.

11. The non-transitory computer-readable medium according to claim 8, wherein the interaction information is derived from implicit interactions.

12. The non-transitory computer-readable medium according to claim 11, wherein implicit interactions comprise one or more of:

a page view, a quick view, a vote on a online survey, an addition of an item to an online shopping cart, a purchase of one or more items, and/or an addition of an item to a private catalog.

13. The non-transitory computer-readable medium according to claim 8, wherein calculating the level of similarity of the first user and the second user comprises calculating an aggregated similarity level based on user interests and product preferences.

14. The non-transitory computer-readable medium according to claim 8, wherein calculating the level of similarity of the first user and the second user comprises correcting the plurality of weights for each of the first user and the second user based upon the number of distinct users associated with each attribute.

15. A system for producing a recommendation for a first user using interaction information for each of the first user and a second user of a plurality of users of a computer network, the interaction information for the first user and the second user derived from interactions of the first user and the second user with the computer network, the system comprising:

at least one processor for communicatively coupling to the first user and the second user, the at least one processor operable to, at least;

characterize the first user and the second user with regard to each of a plurality of attributes, based upon the interaction information for the first user and the second user;

calculate a level of similarity of the first user and the second user, using the characterization of the first user and the characterization of the second user with regard to the plurality of attributes; and

present to the first user a recommendation of a product or a service, based upon the similarity level and the interaction information for the first user and the second user.

16. The system according to claim 15, wherein the computer network comprises an e-commerce system.

17. The system according to claim 15, wherein the interaction information is derived from social networking activities of the first user and the second user over the computer network.

18. The system according to claim 15, wherein the interaction information is derived from implicit interactions.

19. The system according to claim 18, wherein the implicit interactions comprise one or more of:

a page view, a quick view, a vote on a online survey, an addition of an item to an online shopping cart, a purchase of one or more items, and/or an addition of an item to a private catalog.

20. The system according to claim 15, wherein calculating the level of similarity of the first user and the second user comprises calculating an aggregated similarity level based on user interests and product preferences.

21. The system according to claim 15, wherein calculating the level of similarity of the first user and the second user comprises correcting the plurality of weights for each of the first user and the second user based upon the number of distinct users associated with each attribute.