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

2023/03/08 by Wang, Hao
#FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · doi:10.48550/arxiv.2303.04561

Abstract

Collaborative filtering is the simplest but oldest machine learning algorithm in the field of recommender systems. In spite of its long history, it remains a discussion topic in research venues. Usually people use users/items whose similarity scores with the target customer greater than 0 to compute the algorithms. However, this might not be the optimal solution after careful scrutiny. In this paper, we transform the recommender system input data into a 2-D social network, and apply kernel smoothing to compute preferences for unknown values in the user item rating matrix. We unifies the theoretical framework of recommender system and non-parametric statistics and provides an algorithmic procedure with optimal parameter selection method to achieve the goal.

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