Method and system for high performance model-based personalization

• US 8,155,992 B2
• Filed: 08/30/2010
• Issued: 04/10/2012
• Est. Priority Date: 06/23/2000
• Status: Expired due to Term

• Current Assignee: Thalveg Data Flow LLC (Intellectual Ventures LLC)
• Original Assignee: Thalveg Data Flow LLC (Intellectual Ventures LLC)
• Inventors: Ekhaus, Michael A., Driskill, Robert, Mulier, Filip
• Primary Examiner(s): Sterrett, Jonathan G
• Application Number:
  US12/871,391
• Publication Number:
  US 20100332408A1
• Time in Patent Office: 589 Days
• Field of Search: 705/7.29
• US Class Current: 705/7.29
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• 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 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.

Claims

1. A method for determining a recommendation comprising:

banding into bands, by a data processing device, a sparse unary ratings matrix having unary data values representing clients'"'"' ratings, wherein the bands of the sparse unary ratings matrix partition the ratings by client;

distributing the bands to a plurality of computing nodes;

receiving respective output from the plurality of computing nodes, the received output together forming a matrix of co-rates, wherein the matrix of co-rates includes either a pre-multiplication of the sparse unary ratings matrix by a transpose of the sparse unary ratings matrix or a post-multiplication of the sparse unary ratings matrix by the transpose of the sparse unary ratings matrix;

forming in the data processing device a runtime recommendation model from the received output of the plurality of computing nodes;

determining in the data processing device a recommendation from the runtime recommendation model in response to a request; and

generating a recommendation output representative of the recommendation.

2. The method of claim 1, further comprising:

calculating a unary multiplicity voting recommendation from the runtime recommendation model.

3. The method claim 1, further comprising:

calculating a non-unary multiplicity voting recommendation from the runtime recommendation model.

4. The method of claim 2, wherein said calculating a unary multiplicity voting recommendation comprises calculating an anonymous recommendation.

5. The method of claim 2, wherein said calculating a unary multiplicity voting recommendation comprises calculating a personalized recommendation.

6. The method of claim 3, wherein said calculating a non-unary multiplicity voting recommendation comprises calculating an anonymous recommendation.

7. The method of claim 3, wherein said calculating a non-unary multiplicity voting recommendation comprises calculating a personalized recommendation.

15. The method of claim 1, further comprising:

generating, by the plurality of computing nodes, from the bands the respective output that together forms the matrix of co-rates.

8. A method for determining a recommendation comprising:

striping into stripes, by a data processing device, a sparse unary ratings matrix having unary data values representing clients'"'"' ratings, wherein the stripes of the sparse unary ratings matrix partition the ratings by item;

distributing the stripes to a plurality of computing nodes;

receiving respective output from the plurality of computing nodes, the received output together forming a matrix of co-rates, wherein the matrix of co-rates includes either a pre-multiplication of the sparse unary ratings matrix by a transpose of the sparse unary ratings matrix or a post-multiplication of the sparse unary ratings matrix by the transpose of the sparse unary ratings matrix;

forming in the data processing device a runtime recommendation model from the received output of the plurality of computing nodes;

determining in the data processing device a recommendation from the runtime recommendation model in response to a request; and

generating a recommendation output representative of the recommendation.

9. The method of claim 8, further comprising:

calculating a unary multiplicity voting recommendation from the runtime recommendation model.

10. The method claim 8, further comprising:

calculating a non-unary multiplicity voting recommendation from the runtime recommendation model.

11. The method of claim 9, wherein said calculating a unary multiplicity voting recommendation comprises calculating an anonymous recommendation.

12. The method of claim 9, wherein said calculating a unary multiplicity voting recommendation comprises calculating a personalized recommendation.

13. The method of claim 10, wherein said calculating a non-unary multiplicity voting recommendation comprises calculating an anonymous recommendation.

14. The method of claim 10, wherein said calculating a non-unary multiplicity voting recommendation comprises calculating a personalized recommendation.

16. The method of claim 8, further comprising:

generating, by the plurality of computing nodes, from the stripes the respective output that together forms the matrix of co-rates.