Method and system for high performance model-based personalization
View Patent ↗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.
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.
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.
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.
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.