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A Guide to Singular Value Decomposition for Collaborative Filtering

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 recommendations. Singular Value Decomposition (SVD) is one of the popular algorithms used for collaborative filtering. However, directly applying conventional SVD algorithms to collaborative filtering may result in poor performance. In this report, we discuss problems that beginners may face and present effective SVD variants for collaborative filtering.