P-tree singular value decomposition item-feature collaborative filtering algorithm for netix prize

Tingda Lu, Yan Wang, William Perrizo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collaborative Filtering is effective to provide customers with personalized recommendations by analyzing the purchase pattens. Matrix factorization, e.g. Singular Value Decomposition, is another successful technique in recommendation system. We implemented Singular Value Decomposition algorithm to achieve the least total squared errors. Based on the result, item-feature Collaborative Filtering was further designed and implemented in P-Tree data structure. The purpose of item-feature Collaborative Filtering is to achieve the local optimization. Our experiment on Netix Prize data suggests Singular Value Decomposition is good at global optimization and item-feature Collaborative Filtering is good at local optimization. Blending of two algorithms achieves better RMSE score.

Original languageEnglish (US)
Title of host publicationProceedings of the ISCA 26th International Conference on Computers and Their Applications, CATA 2011
Pages15-20
Number of pages6
StatePublished - Dec 1 2011
Event26th International Conference on Computers and Their Applications, CATA 2011 - New Orleans, LA, United States
Duration: Mar 23 2011Mar 25 2011

Publication series

NameProceedings of the ISCA 26th International Conference on Computers and Their Applications, CATA 2011

Other

Other26th International Conference on Computers and Their Applications, CATA 2011
CountryUnited States
CityNew Orleans, LA
Period3/23/113/25/11

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