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Collaborative filtering is the simplest but oldest machine learning algorithmin the field of recommender systems. In spite of its long history, it remains adiscussion topic in research venues. Usually people use users/items whosesimilarity scores with the target customer greater than 0 to compute thealgorithms. However, this might not be the optimal solution after carefulscrutiny. In this paper, we transform the recommender system input data into a2-D social network, and apply kernel smoothing to compute preferences forunknown values in the user item rating matrix. We unifies the theoreticalframework of recommender system and non-parametric statistics and provides analgorithmic procedure with optimal parameter selection method to achieve thegoal.

Kernel-CF: Collaborative filtering done right with social network analysis and kernel smoothing