• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (8): 174-179.

• 论文 • Previous Articles     Next Articles

A shilling attacks detection method of
recommender systems based on hybrid strategies  

LvChengshu1,WANG Weiguo2   

  1. (1.School of Management Science and Engineering,Dongbei University of Finance and Economics,Dalian 116025;
    2.School of Mathematics and Quantitative Economics,Dongbei University of Finance and Economics,Dalian 116025,China)
  • Received:2012-05-30 Revised:2012-08-23 Online:2013-08-25 Published:2013-08-25

Abstract:

Detecting shilling attacks for recommender systems is necessary to solve problems such as imbalanced dataset and cost sensitive, but existing methods are lack of relative studies. This paper propose a new attack detection method, which combines the methods of undersampling and costsensitive support vector machine together. Firstly, according to the different importance for classification to process, the training dataset is balanced by sample importance based undersampling technique, for the sake of eliminating a lot of noise samples while retaining the most of useful samples. Secondly, costsensitive support vector machine is conducted to train the reconstructed dataset. Finally, the detection decision function is obtained. Experimental results show that the proposed method can improve the accuracy of detecting shilling attacks and has a strong generality.

Key words: recommender system;shilling attack;unbalanced data;costsensitive learning; undersampling;support vector machine