Experimental study on item-based P-tree collaborative filtering algorithm for Netflix Prize

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

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

3 Scopus citations

Abstract

Recommendation system provides customer with personalized recommendations by analyzing historical transactions hence identifying the mostly possible item(s) that will be of interest to the customer. Collaborative Filtering (CF) algorithm plays an important role in the recommendation system. Item-based collaborative filtering is widely employed over user-based algorithm due to the computational complexity. Item similarity calculation is the first but the most important step in item-based collaborative filtering algorithm. In this paper, we analyze and implement several item similarity measurements in P-Tree format to Netflix Prize data set. Our experiments suggest that adjusted cosine based similarity provides much better RMSE than other item-based similarity measurements. The experimental results provide a guideline for our next step for Netflix Prize.

Original languageEnglish (US)
Title of host publication18th International Conference on Software Engineering and Data Engineering 2009, SEDE 2009
Pages149-154
Number of pages6
StatePublished - Dec 1 2009
Event18th International Conference on Software Engineering and Data Engineering 2009, SEDE 2009 - Las Vegas, NV, United States
Duration: Jun 22 2009Jun 24 2009

Publication series

Name18th International Conference on Software Engineering and Data Engineering 2009, SEDE 2009

Other

Other18th International Conference on Software Engineering and Data Engineering 2009, SEDE 2009
CountryUnited States
CityLas Vegas, NV
Period6/22/096/24/09

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