Large-scale Collaborative Filtering Algorithms
Date Issued
2008
Date
2008
Author(s)
Ma, Chih-Chao
Abstract
As the market of electronic commerce grows explosively, it is important to provide customized suggestions for various consumers. Collaborative filtering is an important technique which models and analyzes the preferences of customers, and gives suitable advices. In this thesis, we study large-scale collaborative filtering algorithms to process huge data sets in acceptable time. We use the well-known Singular Value Decomposition as the basis of our algorithms, and propose some improvements. We also discuss post-processing methods. We participate at the competition of Netflix Prize, a contest of predicting movie preferences, and achieve good results.
Subjects
collaborative filtering
recommendation system
singular value decomposition
matrix factorization
Type
thesis
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