推荐系统中典型用户群组的发现和应用

摘要

推荐系统是解决用户的个性化信息需求的一种有效工具。但随着推荐系统用户规模的扩大，需要合理地从海量用户中筛选出用户子集，并进行持续和深入的分析以改进推荐系统。因此，文中首先提出典型用户群组的概念，以期发现推荐系统中的典型用户子集，从而可正确地反映全体用户的兴趣偏好。随后提出一种典型用户群组的发现算法，通过比较候选新增典型用户对典型用户群组的贡献度，逐一扩大典型用户群组规模，最终达到较高的推荐项目覆盖率和评分准确度。最后在典型用户群组中寻找用户的最近邻，实现一种改进的协同过滤推荐算法。通过在真实数据集上的实验结果表明，与其他用户群组发现算法以及经典推荐算法相比，验证典型用户群组不仅具有较好的代表性，也能够获得更好的推荐效果。%Recommender system ( RS) provides an effective way to solve the personalized information needs of users. However, with the expansion of the user scale, it is necessary to find some subsets of vast amounts of RS users, and the continuous and in-depth analysis for these user subsets can be used to improve the RS. Therefore, the typical user group ( TUG) is defined as a representative subset of the entire users in RS to correctly reflect the preferences of all the users. Then, a weighted typical user group finding algorithm ( WTFA) is designed to compare the contributions of the candidate typical users and choose the typical users with higher contribution, so that a TUG is built with high item coverage rate and rating accuracy. A modified TUG-based collaborative filtering( TUG-CF) algorithm is developed to discover the nearest neighbors in TUG. The experimental results on real world dataset show that TUG is better than most rating user group and maximizes diversified user group on item coverage rate and rating accuracy, and TUG-CF has better recommendation results than traditional collaborative filtering methods.