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Frontiers of Computer Science

, Volume 13, Issue 2, pp 231–246 | Cite as

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

  • Min GaoEmail author
  • Bin Ling
  • Linda Yang
  • Junhao Wen
  • Qingyu Xiong
  • Shun Li
Research Article
  • 30 Downloads

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 

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Notes

Acknowledgements

This research is supported by 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), and the China Scholarship Council.

Supplementary material

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Min Gao
    • 1
    • 2
    Email author
  • Bin Ling
    • 3
  • Linda Yang
    • 3
  • Junhao Wen
    • 1
    • 2
  • Qingyu Xiong
    • 1
    • 2
  • Shun Li
    • 1
    • 2
  1. 1.Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University)Ministry of EducationChongqingChina
  2. 2.School of Software EngineeringChongqing UniversityChongqingChina
  3. 3.School of EngineeringUniversity of PortsmouthPortsmouthUK

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