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Differential Privacy and Bayesian for Context-Aware Recommender Systems

Differential Privacy and Bayesian for Context-Aware Recommender Systems

Shuxin Yang, Kaili Zhu
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 13
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa2
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MLA

Yang, Shuxin, and Kaili Zhu. "Differential Privacy and Bayesian for Context-Aware Recommender Systems." IJCINI vol.15, no.4 2021: pp.1-13. http://doi.org/10.4018/IJCINI.20211001.oa2

APA

Yang, S. & Zhu, K. (2021). Differential Privacy and Bayesian for Context-Aware Recommender Systems. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-13. http://doi.org/10.4018/IJCINI.20211001.oa2

Chicago

Yang, Shuxin, and Kaili Zhu. "Differential Privacy and Bayesian for Context-Aware Recommender Systems," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-13. http://doi.org/10.4018/IJCINI.20211001.oa2

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Abstract

Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.