融入个体特征差异的鲁棒协同过滤推荐算法

Robust collaborative filtering recommendation algorithm incorporated with the difference of individual features

针对现有推荐算法鲁棒性差的问题,提出一种融入个体特征差异的鲁棒协同过滤推荐算法.首先,根据用户评分信息的分布情况,给出用户评分个数偏离度和用户近邻平均相似度两个个体特征计算方法;然后基于真实用户和攻击用户个体特征的差异性,提出一种可疑用户标记算法;最后将可疑用户标记算法与矩阵分解技术相结合,对目标用户进行推荐.在Movie Lens数据集上通过实验比较了提出的算法和其他相关算法的性能,实验结果表明算法不仅能够提高推荐精度,而且具有较强的鲁棒性.

The existing recommendation algorithms have poor robustness against shilling attacks.In this con-sideration,in this paper we propose a robust recommendation algorithm incorporated with the difference of indi-vidual features.We first give two individual features which are user′s deviation degree of rating numbers and av-erage similarity of user′s neighbors.According to the distribution of users′ratings,we introduce the computation-al methods of individual features.Then we give the algorithm which can be used to label suspicious users based on the differences of computational results of individual features.Finally,we incorporate the matrix factorization technology with the identification results of suspicious users to make recommendations for users.Experimental re-sults show that the proposed algorithm not only improves the recommendation accuracy, but also has better ro-bustness.