Extension study on item-based P-Tree collaborative filtering algorithm for netflix prize

Tingda Lu, Yan Wang, William Perrizo, Amal Perera, Gregory Wettstein

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

2 Scopus citations

Abstract

Recommendation systems provide customers with personalized recommendations by analyzing the purchase history. Item-based Collaborative Filtering (CF) algorithms recommend items which are similar to what the customers purchased before. Item-based algorithms are widely employed over user-based algorithms due to less computational complexity and better accuracy. We implement several types of item-based CF algorithms with P-Tree data structure. Similarity corrections and item effects are further included in the algorithms to exclude support and item variance. Our experiment on Netflix Prize data suggests support based similarity corrections and item effects have an significant impact on the prediction accuracy. Pearson and SVD item-feature similarity algorithms with support based similarity correction and item effects achieve better RMSE scores.

Original languageEnglish (US)
Title of host publication25th International Conference on Computers and Their Applications 2010, CATA 2010
Pages180-185
Number of pages6
StatePublished - Dec 1 2010
Event25th International Conference on Computers and Their Applications 2010, CATA 2010 - Honolulu, HI, United States
Duration: Mar 24 2010Mar 26 2010

Publication series

Name25th International Conference on Computers and Their Applications 2010, CATA 2010

Other

Other25th International Conference on Computers and Their Applications 2010, CATA 2010
CountryUnited States
CityHonolulu, HI
Period3/24/103/26/10

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