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.

翻译：协同过滤是推荐系统领域中最简单但历史最悠久的机器学习算法。尽管其历史悠久，但至今仍是学术研究的热点议题。传统方法通常选取与目标用户相似度大于零的用户/物品参与算法计算，然而经严格论证，这种方案并非最优解。本文通过将推荐系统输入数据转化为二维社交网络，并运用核平滑技术对用户-物品评分矩阵中的未知值进行偏好估计。该研究首次统一了推荐系统与非参数统计学的理论框架，提出了具有最优参数选择方法的算法流程，从而实现了研究目标。