Affiliations: [a]
School of Geography and Tourism, Anhui Normal University, Wuhu, Anhui, China
| [b]
School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China
| [c] Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China
Correspondence:
[*]
Corresponding author. Yonglong Luo. E-mail: [email protected].
Abstract: Adding noise to user history data helps to protect user privacy in recommendation systems but affects the recommendation performance. To solve this problem, a matrix factorization tourism point of interest recommendation model based on interest offset and differential privacy is proposed in this paper. The recommendation performance of the model is improved by analyzing user interest preferences. Specifically, user interest offsets are extracted from user tags and user ratings under time-series factors to calculate user interest drift. Then, similar neighbors are found to train user feature preferences which are integrated into the matrix model in the form of regular terms. Meanwhile, based on the differential privacy theory, a privacy neighbor selection algorithm combining the K-Medoides clustering algorithm and index mechanism is designed to effectively protect the identity of neighbors and prevent KNN attacks. Besides, the Laplace mechanism is used to implement differential privacy protection for the model’s gradient descent process. Finally, the feasibility of the proposed recommendation model is verified through experiments, and the experimental results indicate that this model has advantages in recommendation accuracy and privacy protection.
