Method and system of information matching in electronic commerce website

• US 8,346,782 B2
• Filed: 08/18/2010
• Issued: 01/01/2013
• 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:
  US12/937,555
• Publication Number:
  US 20120143816A1
• Time in Patent Office: 867 Days
• Field of Search: 707748-759, 707764-771
• 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

The present disclosure describes an information matching method and information system. The method collects characteristic data of category plurality of categories of network behavior of a plurality of network users; groups the network users into a plurality of clusters based on the characteristic data; sets weight of the characteristic data; receives a search request from one of the plurality of network users; obtains search results according to the search request; inquires a historical record of clicking operation on at least some of the search results made by at least some of the network users in one of the clusters which contains the one of the plurality of network users; calculates a respective rank value of each of the at least some of the search results based on the historical record of clicking operation of at least some of the network users and the weight of the characteristic data; sorts of the search results to arrange the search results according to the rank values of the search results in a descending order from high rank to low rank; and causes the sorted search results to be displayed.

Claims

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

collecting characteristic data of network behavior of a plurality of users;

grouping multiple users of the plurality of users into one cluster, the multiple users not having the collected characteristic data of network behavior;

grouping remaining users of the plurality of users into one or more predetermined clusters based on the characteristic data of network behavior;

storing results of the one cluster and the one or more predetermined clusters in a database;

setting weight of the characteristic data;

receiving a search request from a user device of a user of the plurality of users;

obtaining search results based on the search request;

inquiring a historical record of clicking operation on multiple search results of the search results, the clicking operation being made by a plurality of users in a cluster that contains the user;

calculating rank values of the multiple search results based on the historical record of the clicking operation and the weight of the characteristic data;

sorting the multiple search results based on the rank values in a descending order from a high rank to a low rank; and

causing the sorted search results to be displayed.

2. The method of claim 1, wherein the network behavior is associated with online transactions or web comments, and wherein the characteristic data is associated with the online transactions or the web comments.

3. The method of claim 1, wherein the grouping the remaining users of the plurality of users into one or more predetermined clusters based on the characteristic data of network behavior comprises:

if the characteristic data of the network behavior is associated with online transactions, grouping the remaining users based on multiple items associated with the online transactions, the multiple items sharing one or more properties.

4. The method of claim 1, wherein the grouping the remaining users of the plurality of users into one or more predetermined clusters based on the characteristic data of network behavior comprises:

if the characteristic data of the network behavior is associated with web comments, grouping the remaining users based on at least one of users receiving the web comments or

a number of the remaining users who make the web comments against the users receiving the web comments and a number of the plurality of users who make the web comments against the users receiving the comments.

5. The method of claim 1, wherein the collecting characteristic data of network behavior of a plurality of users comprises:

collecting the characteristic data based on at least one of server log, network activities log, geographical information, or an interface of a third party.

6. The method of claim 1, further comprising:

setting weight of geographical position information, wherein the calculating rank values of the multiple search results comprises calculating the rank values of the multiple search results based on the weight of geographical position information.

7. The method of claim 1, wherein the receiving a search request from the user comprises receiving a key word or selection one user of the plurality of users as the search request.

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

collecting activity records of a plurality of users;

grouping multiple users of the plurality of users into one cluster, the multiple users not having an activity record;

grouping remaining users of the plurality of users into one or more predetermined clusters based on the collected activity records;

storing results of the one cluster and the one or more predetermined clusters in a database;

setting weight of the activity records;

receiving a search query from a user device associated with a user of the plurality users;

obtaining multiple results based on the search query;

retrieving a historical record of clicking operation on one or more results of the multiple results, the clicking operation being made by users of a cluster that includes the user;

calculating rank values of the multiple results based on the historical record of clicking operation and the weight of the activity records associated with the cluster;

sorting the multiple results based on the rank values in a descending order from a high rank to a low rank;

enabling a display of the sorted search results.

9. The one or more non-transitory computer-readable storage medium of claim 8, wherein the activity records include activities associated with at least one of online transactions or web comments.

10. The one or more non-transitory computer-readable storage medium of claim 8, wherein the grouping other users of the plurality of users into the one or more predetermined clusters based on the collected activity records comprises:

grouping the other users of the plurality of users into the one or more predetermined clusters based on multiple items associated with online transactions, the multiple items sharing one or more properties.

11. The one or more non-transitory computer-readable storage medium of claim 8, wherein a number of the one or more predetermined clusters is less than a predetermined number.

12. The one or more non-transitory computer-readable storage medium of claim 8, wherein the collecting the activity records of the plurality of users comprises collecting the activity records based on at least one of a server log, a network log, or geographical information.

13. The one or more non-transitory computer-readable storage medium of claim 8, wherein the calculating the rank values of the multiple results comprises calculating the rank values further based on geographical information associated with the plurality of users.

14. The one or more non-transitory computer-readable storage medium of claim 8, wherein the search query includes at least one of a keyword or selection of the user.

15. 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;

collect online activity records of a plurality of users;

grouping multiple users of the plurality of users into one cluster, the online activity records of the multiple users being not available;

grouping remaining users of the plurality of users into one or more clusters based on the collected online activity records;

storing results the one cluster and the one or more clusters in a database;

setting weight of the online activity records;

receiving a search query from a user device associated with a user of the plurality users;

obtaining multiple results based on the search query;

retrieving a historical record of clicking operation on one or more results of the multiple results, the clicking operation being made by users of a cluster that includes the user;

calculating rank values of the multiple results based on the historical record of clicking operation and the weight of the online activity records associated with the cluster;

sorting the multiple results based on the rank values in a descending order from a high rank to a low rank, and

enabling the multiple results to be displayed.

16. The computer system of claim 15, wherein the collecting the online activity records of the plurality of users comprises collecting the online activity records based on at least one of a server log, a network log, or geographical information.

17. The computer system of claim 15, wherein the calculating the rank values of the multiple results comprises calculating the rank values further based on geographical information associated with the plurality of users.

18. The computer system of claim 15, wherein the search query includes at least one of a keyword or selection of the user.

19. The computer system of claim 15, wherein the activity records include activities associated with at least one of online transactions or web comments.

20. The computer system of claim 15, wherein a number of the one or more predetermined clusters is less than a predetermined number.