  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.
