CONTENT BASED METHOD FOR PRODUCT-PEER FILTERING

• US 20010049623A1
• Filed: 10/09/1998
• Published: 12/06/2001
• Est. Priority Date: 10/09/1998
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: AGGARWAL, CHARU C., YU, PHILIP S.
• Granted Patent: US 6,356,879 B2
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0253 During e-commerce, i.e. online transactions
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

The present invention derives product characterizations for products offered at an e-commerce site based on the text descriptions of the products provided at the site. A customer characterization is generated for any customer browsing the e-commerce site. The characterizations include an aggregation of derived product characterizations associated with products bought and/or browsed by that customer. A peer group is formed by clustering customers having similar customer characterizations. Recommendations are then made to a customer based on the processed characterization and peer group data.

Claims

1. A method for providing product recommendations to customers in an e-commerce environment, comprising the steps of:

(a) deriving product characterizations for each of said plurality of products;

(b) creating individual customer characterizations on each of said customers based on usage of said product characterizations by each of said respective customers;

(c) clustering based on similarities in said customer characterizations, to form peer groups;

(d) categorizing individual customers into one of said peer groups; and

(e) making product recommendations to customers based on said customer characterizations and information from said categorized peer groups.

2. The method according to

claim 1

, wherein the step of creating customer characterizations includes extracting product characterizations when said customers browse or purchase said products.

4. The method according to

claim 1

, wherein the step of deriving product characterizations includes use of text characterizations associated with said plurality of products.

5. The method according to

claim 1

, wherein said step of creating product characterizations further includes the step of:

(a) finding a frequency of occurrence for each word in said text descriptions;

(b) dividing the total frequency for each word by the frequency of occurrence for said word for all customers;

(c) finding the standard deviation for each word;

(d) selecting words having larger standard deviations; and

(e) expressing a product characterization based on said selected words.

6. The method of

claim 1

, further including the steps of:

(a) deriving product characterizations based on a current on-line session;

(b) accessing historical product characterizations from memory;

(c) creating a customer characterization by weighted concatenating characterizations from steps (a) and (b);

(d) computing a cluster centroid for each of said peer groups;

(e) selecting the peer groups whose cluster centroid is closest to the characterization created in step (c); and

(f) generating one of product, peer and profile recommendations based on said selected peer group.

7. The method of

claim 6

, wherein said cluster centroid is computed by concatenating text characterizations of all customer characterizations in each peer group.

8. The method of

claim 6

, wherein the recommendations comprise a weighted concatenation of text-characterizations of products bought and browsed in said current on-line session.

3. The method according to claim 27 wherein the step of creating customer characterizations further includes concatenating each of the product characterizations of all products browsed or purchased by an individual customer.

9. A stored program device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform process steps for providing product recommendations to customers in an e-commerce environment, comprising the steps of:

accessing product information on products offered for sale on an e-commerce site;

deriving product characterizations from said product information;

accessing customer usage information relating to said products;

creating customer characterization on each of said customers based on said accessed usage information;

clustering based on similarities in said customer characterizations, to form a plurality of peer groups;

receiving queries from a current session customer;

creating present customer characterizations associated with said current session customer;

categorizing said current session customer into a selected peer group based on similarities in the present customer characterizations and past customer characterizations;

responding to said queries with product recommendations to said current session customer based at least on information from said categorized selected peer group.

10. The device according to

claim 9

, further including stored programs for causing said computer to extract data exchanged between said current session customer and said site to create present customer characterizations.

11. The device according to

claim 9

, wherein the step of creating customer present characterizations further includes weighted concatenating each of the product characterizations of all products browsed or purchased by said present session customer.

12. The device of

claim 9

, further including stored programs for causing said computer to:

compute a cluster centroid for each of said peer groups by concatenating text characterizations of all customer characterizations in each peer group;

select the peer group having a cluster centroid closest to the present customer characterizations; and

generate one of product, peer and profile recommendations based on data from said selected peer group.

13. In a computer having a processor and stored program for causing the computer to interact with customers in an e-commerce environment and to provide answers to customer inquiries, said stored program comprising:

means for deriving product characterizations for a plurality of products;

means for creating customer characterizations based on usage of said product characterizations by said customers;

means for clustering, based on similarities in said customer characterizations, for forming a plurality of peer groups;

means for placing individual customers into one of said peer groups; and

means for providing answers to said customer queries based on said customer characterizations and information from said peer groups.

14. The stored program of

claim 13

, wherein said means for creating customer characterizations includes storage for archiving said customer characterizations.

15. The stored program of

claim 13

, further including means for creating present customer characterizations for a currently on-line customer based on a concatenation of data extracted from usage of product characterizations by the currently on-line customer.

16. The stored program of

claim 15

, further including means for placing a currently on-line customer in one of said peer groups based on similarities in said present customer characterizations and stored characterizations.

17. The stored program of

claim 13

, wherein said means for deriving product characterizations include means for extracting text descriptions associated with said products.

18. The stored program of

claim 13

, wherein said means for providing answers includes means for recommending products to an individual customer based on product characterizations from products browsed by said individual customer in a current session.

19. The stored program of

claim 13

, wherein said means for providing answers includes means for recommending products to an individual customer based on a weighted frequency of historic product characterizations and a weighted frequency of present product characterizations from products browsed by said individual customer in a current session.

20. The stored program of

claim 13

, wherein said means for providing answers includes means for recommending products to an individual customer based on historic product characterizations.

21. The stored program of

claim 13

, wherein said means for providing answers includes means for recommending products to an individual customer based on products in a promotion list.

22. The stored program of

claim 13

, wherein said means for providing answers includes providing a peer distribution profile based on product frequency for one of said peer groups.