论文标题
用于协作过滤的精制SVD算法
A Refined SVD Algorithm for Collaborative Filtering
论文作者
论文摘要
协作过滤试图根据其他品味相似的用户的意见来预测用户对某些项目的评分。等级通常以稀疏矩阵的形式给出，目的是找到缺失的条目（即评级）。存在各种协作过滤的方法，一些最受欢迎的方法是单数值分解（SVD）和K-均值聚类。 SVD方法的挑战之一是找到对未知评分的良好初始化。 [1]提出了可能的初始化。在本文中，我们解释了如何使用K均值方法来实现SVD初始化的进一步完善。我们表明，我们的技术的表现优于单独使用的两种初始化技术。
Collaborative filtering tries to predict the ratings of a user over some items based on opinions of other users with similar taste. The ratings are usually given in the form of a sparse matrix, the goal being to find the missing entries (i.e. ratings). Various approaches to collaborative filtering exist, some of the most popular ones being the Singular Value Decomposition (SVD) and K-means clustering. One of the challenges in the SVD approach is finding a good initialization of the unknown ratings. A possible initialization is suggested by [1]. In this paper we explain how K-means approach can be used to achieve the further refinement of this initialization for SVD. We show that our technique outperforms both initialization techniques used separately.
