关键词: 推荐系统, 非负矩阵分解, 托攻击, 检测算法

Abstract: The existing unsupervised detection algorithms have a high misjudgment rate for normal user. To solve this problem, a method for detecting shilling attack based on nonnegative matrix factorization is proposed. Firstly, features of the user are extracted from the nonnegative matrix factorization technique. Secondly, the K-means clustering method is used to extract the initial normal user set and the initial shilling attack set. Finally, using features of initial normal user set classifies the initial shilling attack set, to detect shilling attacks. Experimental results show that this algorithms are more effective in detecting the attacks compared to other algorithms.

Key words: recommender systems, nonnegative matrix factorization, shilling attack, detection algorithm