Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

作     者：张响亮 Tak Man Desmond Lee Georgios Pitsilis 

作者机构：King Abdullah University of Science and Technology Faculty of Science Technology and Communication University of Luxembourg 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2013年第28卷第4期

页      面：616-624页

核心收录：

学科分类：0808[工学-电气工程] 081203[工学-计算机应用技术] 08[工学] 0839[工学-网络空间安全] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The preliminary version of the paper was published in the Proceedings of EDB2012 

主　　题：shilling attack recommender system collaborative filtering social trust clustering 

摘      要：Abstract Recommender systems （RS） have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CLUTR and WCLUTR, to combine clustering with ＂trust＂ among users. We demonstrate that CLuTR and WCLUTR enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system ***.