From similarity perspective: a robust collaborative filtering approach for service recommendations

作     者：Min GAO Bin LING Linda YANG Junhao WEN Qingyu XIONG Shun LI 

作者机构：Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education Chongqing 400044 China School of Software Engineering Chongqing University Chongqing 400044 China School of Engineering University of Portsmouth Portsmouth PO1 3 AH UK 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2019年第13卷第2期

页      面：231-246页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：the Basic and Advanced Research Projects in Chongqing (cstc2015jcyjA40049) the National Natural Science Foundation of China (Grant No. 71102065) the Fundamental Research Funds for the Central Universities (106112014 CDJZR 095502) the China Scholarship Council 

主　　题：collaborative filtering service recommendation system robustness shilling attack 

摘      要：Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions. We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.