9th EAI International Conference on Mobile Multimedia Communications

Research Article

Trust-aware Privacy-Preserving Recommender System

  • @INPROCEEDINGS{10.4108/eai.18-6-2016.2264146,
        author={Xiwei Wang and Jun Zhang and Yin Wang},
        title={Trust-aware Privacy-Preserving Recommender System},
        proceedings={9th EAI International Conference on Mobile Multimedia Communications},
        keywords={collaborative filtering nonnegative matrix factorization privacy recommender system trust},
  • Xiwei Wang
    Jun Zhang
    Yin Wang
    Year: 2016
    Trust-aware Privacy-Preserving Recommender System
    DOI: 10.4108/eai.18-6-2016.2264146
Xiwei Wang1,*, Jun Zhang2, Yin Wang3
  • 1: Northeastern Illinois University
  • 2: University of Kentucky
  • 3: Lawrence Technological University
*Contact email: xwang9@neiu.edu


Recommender systems have achieved great success in providing product recommendations for online shopping. With recommender systems, customers can find their interested merchandise in a timely manner. It not only facilitates customers' purchases, but also promotes sales. While recommender systems can predict customers' preferences accurately, they suffer from privacy leakage in many aspects.

In this paper, we study an attack model in centralized recommender systems and present a privacy-preserving recommendation framework to neutralize such attack. In this model, an attacker holds some of the real customers' ratings and attempts to obtain their private preferences. The proposed framework is based on the weighted nonnegative matrix tri-factorization technique. It utilizes customers' trustworthiness to filter out unrelated ratings so that their privacy can be preserved. We performed experiments on two different datasets with respect to recommendation precision and privacy preservation level. The results demonstrate that our recommender system can distinguish between real customers and attackers to a great extent without compromising the prediction accuracy.