文章基本信息

标题：A Robust Collaborative Recommendation Algorithm Based on Least Median Squares Estimator
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作者：Zhang, Fuzhi ; Sun, Shuangxia
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2014
卷号：9
期号：2
页码：308-314
DOI：10.4304/jcp.9.2.308-314
语种：English
出版社：Academy Publisher
摘要：The existing matrix factorization based collaborative recommendation algorithms have lower robustness against shilling attacks. With this problem in mind, in this paper we propose a robust collaborative recommendation algorithm based on least median squares estimator. We first propose a method of weight calculation to filter out the largest residuals by introducing the least median squares estimator (LMedS-estimator) of robust statistics, which can reduce the increment of target item’s feature vector caused by shilling attacks. Then we apply the method of weight calculation to RLS-estimator in order to realize the robust estimate of user feature matrix and item feature matrix. Finally, we develop a robust collaborative recommendation algorithm to make predictions. Experimental results on two different-scale MovieLens datasets show that the proposed algorithm outperforms the existing methods in terms of both the prediction accuracy and robustness.
关键词：shilling attacks;robust collaborative recommendation algorithm;least median squares estimator;reweighted least squares estimator;robustness