System and method for an electronic product advisor

• US 8,214,264 B2
• Filed: 05/02/2006
• Issued: 07/03/2012
• Est. Priority Date: 05/02/2005
• Status: Active Grant

• Current Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Original Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Inventors: Kasavin, Greg, Bedard, Scott, Andrus, Patrick Cashman
• Primary Examiner(s): ROSEN, ELIZABETH H
• Application Number:
  US11/415,416
• Publication Number:
  US 20060282304A1
• Time in Patent Office: 2,254 Days
• Field of Search: 705/10, 705/26, 705/27
• US Class Current: 705/26.7
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0217 involving input on products or services in exchange for incentives or rewards
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0623 Item investigation
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A system and method operates on a client device and acquires a suspect list of user products based on information derived from the client device. The system normalizes the list, and the user confirms the accuracy of the product list. The user product list is sent to a server where the user product list is compared to other lists using collaborative filtering techniques. The collaborative filtering techniques determine products of interest for the use and the level of interest of the user. The system computes a similarity measure based upon the number of similar products that match the user'"'"'s product list and rankings provided by the user and others. Demographic and behavioral data may also be used in performing the comparison and the similarity measure. The system acquires editorial rankings of products from other users and provides a ranked list of recommended products based upon the editorial rankings.

Claims

1. A computer-implemented method for product recommendation, the method comprising:

by at least one computer system,

determining a similarity percentage between a first list of products and each of a plurality of a second list of products, wherein each product that appears on at least one second list and does not appear on the first list is a candidate product;

for each candidate product,

determining the sum, across the second lists, of each instance of the candidate product weighted by the determined similarity percentage for the second list upon which the instance of the candidate product appears; and

recommending candidate products in order of determined sum of the candidate product.

2. The method of claim 1, wherein:

the similarity percentage between a first list and each second list is the percentage of first list products that appear on the second list.

3. The method of claim 1, wherein:

each of the first list and the second lists is characterized by the same categorization.

4. The method of claim 1, further comprising, prior to determining a similarity percentage:

acquire the first list.

5. The method of claim 4, wherein acquiring the first list comprises:

scan an electronic device associated with a first user for the first list.

6. The method of claim 5, wherein acquiring the first list comprises:

scanning an electronic device associated with a first user for the products to be listed.

7. The method of claim 1, wherein:

recommending comprises displaying the list of recommended products to a user.

8. A computer program product for product recommendation, the computer program product comprising:

non-transmission media memory; and

instructions, stored on the non-transmission media memory, that when executed by at least one processor;

determine a similarity percentage between a first list of products and each of a plurality of a second list of products, wherein each product that appears on at least one second list and does not appear on the first list is a candidate product;

for each candidate product,

determine the sum, across the second lists, of each instance of the candidate product weighted by the determined similarity percentage for the second list upon which the instance of the candidate product appears; and

recommend candidate products in order of determined sum of the candidate product.

9. The computer program product of claim 8, wherein:

the similarity percentage between the first list and each second list is the percentage of first list products that appear on the second list.

10. The computer program product of claim 8, wherein:

each of the first list and the second lists is characterized by the same categorization.

11. The computer program product of claim 8, further comprising, prior to determining a similarity percentage:

acquire the first list.

12. The computer program product of claim 11, wherein acquiring the first list comprises:

scan an electronic device associated with a first user for the first list.

13. The computer program product of claim 12, wherein acquiring the first list comprises:

scanning an electronic device associated with a first user for the products to be listed.

14. The computer program product of claim 8, wherein:

recommending comprises displaying the list of recommended products to a user.

15. A system for product recommendation, the system comprising:

at least one processor; and

instructions that when executed by the at least one processor;

determine a similarity percentage between a first list of products and each of a plurality of a second list of products, wherein each product that appears on at least one second list and does not appear on the first list is a candidate product;

for each candidate product,

determine the sum, across the second lists, of each instance of the candidate product weighted by the determined similarity percentage for the second list upon which the instance of the candidate product appears; and

recommend candidate products in order of determined sum of the candidate product.

16. The system of claim 15, wherein:

the similarity percentage between the first list and each second list is the percentage of first list products that appear on the second list.

17. The system of claim 15, wherein:

each of the first list and the second lists is characterized by the same categorization.

18. The system of claim 15, further comprising, prior to determining a similarity percentage:

acquire the first list.

19. The system of claim 18, wherein acquiring the first list comprises:

scan an electronic device associated with a first user for the first list.

20. The system of claim 19, wherein acquiring the first list comprises:

scanning an electronic device associated with a first user for the products to be listed.

21. The system of claim 15, wherein:

recommending comprises displaying the list of recommended products to a user.

22. A computer-implemented method for product recommendation, the method comprising:

by at least one computer system,

determining a similarity percentage between a first set of products and each of a plurality of a second set of products, wherein each product in the at least one second set and not in the first set is a candidate product;

for each candidate product,

determining the sum, across the second sets, of each instance of the candidate product weighted by the determined similarity percentage for the second set in which the instance of the candidate product appears; and

recommending candidate products in order of determined sum of the candidate product.