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

doi:10.1007/s11704-017-6566-y

Front. Comput. Sci.

2019, Vol. 13

Issue (2) : 231-246 https://doi.org/10.1007/s11704-017-6566-y

RESEARCH ARTICLE

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

Min GAO^1,²(

), Bin LING³, Linda YANG³, Junhao WEN^1,², Qingyu XIONG^1,², Shun LI^1,²

¹. Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing 400044, China
². School of Software Engineering, Chongqing University, Chongqing 400044, China
³. School of Engineering, University of Portsmouth, Portsmouth PO1 3AH, UK

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Abstract

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.

Keywords collaborative filtering service recommendation system robustness shilling attack

Corresponding Author(s): Min GAO

Just Accepted Date: 28 March 2017 Online First Date: 25 May 2018 Issue Date: 08 April 2019

Cite this article:

Min GAO,Bin LING,Linda YANG, et al. From similarity perspective: a robust collaborative filtering approach for service recommendations[J]. Front. Comput. Sci., 2019, 13(2): 231-246.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6566-y
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I2/231

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