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：合作过滤是推荐人系统领域最简单但最古老的机器学习算法,尽管历史悠久,但它仍然是研究地点的一个讨论话题。通常人们使用与目标客户相近的用户/项目与目标客户的比值大于0的用户/项目来计算算法。然而,经过仔细审查后,这也许不是最佳解决办法。在本文中,我们将推荐人系统输入数据转换成二维社会网络,并运用内核平滑来计算用户项目评级矩阵中未知值的偏好。我们统一了推荐人系统和非参数统计的理论框架,并提供具有最佳参数选择方法的算法程序来实现目标。</s>