System and process for generating a selection model for use in personalized non-competitive advertising

• US 20090199233A1
• Filed: 02/01/2008
• Published: 08/06/2009
• Est. Priority Date: 02/01/2008
• Status: Abandoned Application

• Current Assignee: RichRelevance Incorporated
• Original Assignee: RichRelevance Incorporated
• Inventors: Ahuja, Sundeep, Selinger, David, Sunwoo, Albert, Osial, James, Kohn, Tyler, DeCourcey, Michael
• Application Number:
  US12/012,449
• Publication Number:
  US 20090199233A1
• Time in Patent Office: Days
• US Class Current: 725/34
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A process for generating a selection model to be used in providing personalized non-competitive advertising including the steps of collecting data from advertising retail websites regarding product data, collecting data from the originating retail websites regarding user behavior, generating a selection model based on the product data and the transactional data and using the selection model to generate personalized non-competitive advertisements for presentation. An arrangement for the same includes memory for storing product data collected from advertising retail websites and data colleted from originating retail websites regarding user behavior and a controller that generates a selection model based on the product data and the transactional data, wherein the selection model includes data sets identifying similar and popular products, and uses the selection model to generate personalized non-competitive advertisements for presentation to one or more of the users for which user behavior has been collected.

Claims

1. A process for generating a selection model to be used in providing personalized non-competitive advertising comprising the steps of:

collecting data from one or more advertising retail websites regarding product data;

collecting data from one or more originating retail websites regarding user behavior;

generating a selection model based on the product data and the transactional data, wherein the selection model includes a plurality of data sets identifying similar and popular products; and

using the selection model to generate personalized non-competitive advertisements for presentation.

2. The process of claim 1 wherein product data includes user purchases.

3. The process of claim 1 wherein product data includes user product views.

4. The process of claim 1 wherein product data includes user searches and subsequent product transactions including product views, product purchases.

5. The process of claim 1 wherein product data includes similarity between products.

6. The process of claim 1 wherein product data includes similarity between product attributes.

7. The process of claim 1 wherein product behavior includes a product'"'"'s inclusion in user lists.

8. The process of claim 1 wherein the step of using the selection model to generate personalized non-competitive advertisements occurs in real time.

9. The process of claim 1 wherein using the selection model to generate personalized non-competitive advertisements includes using a weighted scoring system identifying correlations between user behavior data and their associated attributes.

11. The arrangement of claim 8 wherein product data includes user purchases.

12. The arrangement of claim 8 wherein product data includes user product views.

13. The arrangement of claim 8 wherein product data includes user searches.

14. The arrangement of claim 8 wherein product data includes user lists.

16. The computer readable medium of claim 14 wherein product data includes user purchases.

17. The computer readable medium of claim 14 wherein product data includes user product views.

18. The computer readable medium of claim 14 wherein product data includes user searches.

19. The computer readable medium of claim 14 wherein product data includes user lists.

20. The computer readable medium of claim 14 wherein using the selection model to generate personalized non-competitive advertisements includes using a weighted scoring system identifying correlations between user behavior data.

10. An arrangement for selecting personalized non-competitive electronic advertising from a plurality of competitive and non-competitive advertisements comprising:

memory for storing product data collected from one or more advertising retail websites and data colleted from the one or more originating retail websites regarding user behavior; and

a controller that generates a selection model based on the product data and the transactional data, wherein the selection model includes a plurality of data sets identifying similar and popular products, and uses the selection model to generate personalized non-competitive advertisements for presentation to one or more of the users for which user behavior has been collected.

15. A computer-readable medium having computer-executable instructions for selecting personalized non-competitive electronic advertising from a plurality of competitive and non-competitive advertisements, the computer-executable instructions causing the arrangement to perform the steps of:

collecting data from one or more advertising retail websites regarding product data;

collecting data from one or more originating retail websites regarding user behavior;

generating a selection model based on the product data and the transactional data, wherein the selection model includes a plurality of data sets identifying similar and popular products; and

using the selection model to generate personalized non-competitive advertisements for presentation to one or more of the users for which user behavior has been collected.