Method and system of information matching in electronic commerce website

• US 8,762,391 B2
• Filed: 11/27/2012
• Issued: 06/24/2014
• Est. Priority Date: 08/27/2009
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Zhang, Xu, Liu, Qing-Yan, Wu, Peng-Song, Ye, Yi-Huo
• Primary Examiner(s): Lee, Wilson
• Application Number:
  US13/685,931
• Publication Number:
  US 20130086052A1
• Time in Patent Office: 574 Days
• Field of Search: None
• US Class Current: 707/748
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Techniques for sorting search results using user characteristic data are described. These techniques may include receiving a query from a user device. A search may be performed based on the query to obtain multiple results. User responses corresponding to the multiple results may be obtained and then grouped to determine multiple users based on similarities among the multiple users. Based on user responses associated with the multiple users, the multiple results may then be ranked.

Claims

1. A computer-implemented method for information matching, the method comprising:

receiving a query from a user device;

performing a search based on the query to obtain multiple results;

determining a first category of multiple categories, the first category being associated with purchase records of online transactions;

determining a second category of the multiple categories, the second category being associated with at least one of comments generated for the online transactions and geographical information associated with the online transactions;

assigning a first weight to the first category;

assigning a second weight to the second category;

determining first multiple users from a plurality of users based on the first category and similarities among the first multiple users;

determining second multiple users from the plurality of users based on the second category and similarities among the second multiple users;

calculating a value for an individual result of the multiple results based on one or more responses of the first multiple users and one or more responses of the second multiple users corresponding to the individual result, the first weight and the second weight, wherein the first weight is different from the second weight; and

ranking the multiple results based on the value.

2. The computer-implemented method of claim 1, wherein the similarities among the first multiple users comprise similarities in commodities that the first multiple users have purchased in the online transactions.

3. The computer-implemented method of claim 1, wherein the similarities among the second multiple users comprise similarities in the comments that the second multiple users have made for the online transactions.

4. The computer-implemented method of claim 1, wherein the user device is associated with a user of the plurality of users.

5. The computer-implemented method of claim 1, wherein the similarities among the first multiple users comprise similarities in characteristics of the first multiple users, and the characteristics comprise at least one of user types and user network behaviors.

6. The computer-implemented method of claim 5, wherein the user types comprise a merchant type and a consumer type.

7. The computer-implemented method of claim 1, wherein determining the first multiple users comprises determining the first multiple users further based on availability of the one or more responses of the first multiple users in a record and a predetermined threshold number of the first multiple users.

8. One or more computer-readable storage medium encoded with instructions that, when executed by a processor of a server, perform acts comprising:

receiving a query;

performing a search based on the query to obtain multiple results;

determining a first category of multiple categories, the first category being associated with purchase records of online transactions;

determining a second category of the multiple categories, the second category being associated with at least one of comments generated for the online transactions and geographical information associated with the online transactions;

assigning a first weight to the first category;

assigning a second weight to the second category;

determining first multiple users from a plurality of users based on the first category and similarities among the first multiple users;

determining second multiple users from the plurality of users based on the second category and similarities among the second multiple users;

calculating a value for an individual result of the multiple results based on one or more responses of the first multiple users and one or more responses of the second multiple users corresponding to the individual result, the first weight and the second weight, wherein the first weight is different from the second weight; and

ranking the multiple results based on the value.

9. The one or more computer-readable storage medium of claim 8, wherein the similarities among the first multiple users comprise similarities in commodities that the first multiple users have purchased during the online transactions, and the similarities among the second multiple users comprises similarities in comments that the second multiple users have made for the online transactions.

10. The one or more computer-readable storage medium of claim 8, wherein the similarities among the first multiple users are associated with characteristics of the first multiple users.

11. The one or more computer-readable storage medium of claim 10, wherein the characteristics comprise at least one of user types and user network behaviors.

12. The one or more computer-readable storage medium of claim 11, wherein the user types comprise a merchant type and a consumer type.

15. The one or more computer-readable storage medium of claim 8, wherein the similarities among the first multiple users comprise similarities in characteristics of the first multiple users, and the characteristics comprise at least one of user types and user network behaviors.

16. The one or more computer-readable storage medium of claim 15, wherein the user types comprise a merchant type and a consumer type.

17. The one or more computer-readable storage medium of claim 8, wherein determining the first multiple users comprises determining the first multiple users further based on availability of the one or more responses of the first multiple users in a record and a predetermined threshold number of the first multiple users.

13. A computer system for information matching, the system comprising:

one or more processors;

memory; and

an information matching application, stored in the memory and executable on the one or more processors, configured to;

receive a query,

perform a search based on the query to obtain multiple results,

determine a first category of multiple categories, the first category being associated with purchase records of online transactions,

determine a second category of the multiple categories, the second category being associated with at least one of comments generated for the online transactions and geographical information associated with the online transactions,

assign a first weight to the first category,

assign a second weight to the second category,

determine first multiple users from a plurality of users based on the first category and similarities among the first multiple users,

determine second multiple users from the plurality of users based on the second category and similarities among the second multiple users,

calculating a value for an individual result of the multiple results based on one or more responses of the first multiple users and one or more responses of the second multiple users corresponding to the individual result, the first weight and the second weight, wherein the first weight is different from the second weight,

and

rank the multiple results based on the value.

14. The computer system of claim 13, wherein the similarities among the first multiple users comprise similarities in commodities that the first multiple users have purchased during the online transactions, and the similarities among the second multiple users comprises similarities in comments that the second multiple users have made for the online transactions.

18. The computer system of claim 13, wherein the similarities among the first multiple users comprise similarities in characteristics of the first multiple users, and the characteristics comprise at least one of user types and user network behaviors.

19. The computer system of claim 13, wherein the user types comprise a merchant type and a consumer type.

20. The computer system of claim 13, wherein determining the first multiple users comprises determining the first multiple users further based on availability of the one or more responses of the first multiple users in a record and a predetermined threshold number of the first multiple users.