Recommendation of network object information to user

• US 20100262692A1
• Filed: 04/09/2010
• Published: 10/14/2010
• Est. Priority Date: 04/13/2009
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Peng, Xinyu, Yang, Zhixiong, Zhang, Jinyin
• Granted Patent: US 8,898,283 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• 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: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Recommending network object information to a user includes, for each of a plurality of network objects, a respective plurality of behavior frequencies by the user is determined; a network object among the plurality of network objects that is of interest to the user is identified, the identification being based at least in part on the respective plurality of behavior frequencies that corresponds to each of the plurality of network objects; and additional information relating to the identified network object is provided to the user.

Claims

1. A method for recommending network object information to a user, comprising:

determining, for each of a plurality of network objects, a respective plurality of behavior frequencies by the user, each of the plurality of behavior frequencies corresponds to a number of times the user conducted a respective one of a plurality of behaviors on said each network object within a period of time;

identifying a network object among the plurality of network objects that is of interest to the user, the identification being based at least in part on the respective plurality of behavior frequencies that corresponds to each of the plurality of network objects; and

providing additional information relating to the identified network object.

2. The method of claim 1, wherein the plurality of network objects comprises a plurality of categories of a product or a plurality of keywords associated with the product.

3. The method of claim 1, wherein the plurality of behaviors includes releasing information on one of the plurality of network objects.

4. The method of claim 1, wherein the plurality of behaviors includes receiving an e-mail containing information on one of the plurality of network objects.

5. The method of claim 1, wherein the plurality of behaviors includes releasing opinion information on one of the plurality of network objects.

6. The method of claim 1, wherein the plurality of behaviors includes retrieving information on one of the plurality of network objects.

7. The method of claim 1, wherein the plurality of behaviors includes searching for information on one of the plurality of network objects.

8. The method of claim 1, wherein identifying a network object among the plurality of network objects that is of interest to the user comprises:

deriving, for each of the plurality of network objects, a respective sum of the respective plurality of behavior frequencies by the user, wherein the respective sum comprises adding together each of the respective plurality of behavior frequencies; and

selecting the network object among the plurality of network objects corresponding to a highest respective sum of the respective plurality of behavior frequencies by the user.

9. The method of claim 1, wherein identifying a network object among the plurality of network objects that is of interest to the user comprises:

assigning each of the respective plurality of behavior frequencies a respective weight;

deriving, for each of the plurality of network objects, a respective sum of the respective weighted plurality of behavior frequencies by the user, wherein the respective sum comprises adding together each of the respective weighted plurality of behavior frequencies; and

selecting the network object among the plurality of network objects corresponding to a to highest respective sum of the respective weighted plurality of behavior frequencies by the user.

10. The method of claim 9, wherein the respective weight comprises a value between 0 and 1.

11. The method of claim 9, wherein deriving, for each of the plurality of network objects, a respective sum of the respective weighted plurality of behavior frequencies by the user is derived by equation:

12. A system for recommending network object information to a user, comprising:

one or more processors configured to;

determine, for each of a plurality of network objects, a respective plurality of behavior frequencies by the user, each of the plurality of behavior frequencies corresponds to a number of times the user conducted one of a respective plurality of behaviors on said each network object within a period of time;

identify a network object among the plurality of network objects that is of interest to the user, the identification being based at least in part on the respective plurality of behavior frequencies that corresponds to each of the plurality of network objects; and

provide additional information relating to the identified network object; and

one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions.

13. The system of claim 12, wherein the plurality of network objects comprises a plurality of categories of a product or a plurality of keywords associated with the product.

14. The system of claim 12, wherein the plurality of behaviors includes releasing information on one of the plurality of network objects.

15. The system of claim 12, wherein the plurality of behaviors includes receiving an e-mail containing information on one of the plurality of network objects.

16. The system of claim 12, wherein the plurality of behaviors includes releasing opinion information on one of the plurality of network objects.

17. The system of claim 12, wherein the plurality of behaviors includes retrieving information on one of the plurality of network objects.

18. The system of claim 12, wherein the plurality of behaviors includes searching for information on one of the plurality of network objects.

19. The system of claim 12, wherein identify a network object among the plurality of network objects that is of interest to the user comprises:

derive, for each of the plurality of network objects, a respective sum of the respective plurality of behavior frequencies by the user, wherein the respective sum comprises adding together each of the respective plurality of behavior frequencies; and

select the network object among the plurality of network objects corresponding to a highest respective sum.

20. The system of claim 12, wherein identify a network object among the plurality of network objects that is of interest to the user comprises:

assign each of the respective plurality of behavior frequencies a respective weight;

derive, for each of the plurality of network objects, a respective sum of the respective weighted plurality of behavior frequencies by the user, wherein the respective sum comprises adding together each of the respective weighted plurality of behavior frequencies; and

select the network object among the plurality of network objects corresponding to a highest respective sum.