A Privacy-Aware Multi-Preference-Based Collaborative Filtering Recommendation System with LSH

Abstract

Recommendation systems have been widely used in many internet communities, as they could provide users with personalized items based on the big data analysis technologies. Collaborative Filtering (CF) recommendation algorithm is a well-known and widely used technique in the recommendation systems. However, traditional collaborative filtering methods usually make predictions only based on the rating-based features, which could not make a better understanding of user preferences. Moreover, with the growing disclosure of online personal information, privacy protection is another important issue to be considered in dynamic cyber networks. Thus, a security-aware prediction mechanism is necessary to deal with the data privacy in recommendation systems. In view of these challenges, this paper proposes a privacy-aware multi-preference-based collaborative filtering recommendation method with Locality-Sensitive Hashing (LSH). Firstly, two types of user representations are obtained based on both rating features and latent factors. To protect the privacy data of users, both the LSH technique and differential privacy data perturbation mechanism are integrated into our recommendation model. Besides, the employment of LSH in establishing users' nearest neighbor matrix also improve the similarity searching efficiency of the CF recommendation algorithm. Final recommendation is based on a SGD-based hybrid collaborative prediction model. Experimental results show that our proposed algorithm can improve the accuracy of the recommendation system and reduce the time cost on the basis of ensuring user privacy.