A privacy‐preserving recommendation method with clustering and locality‐sensitive hashing

Abstract

Nowadays, there is a significant increase in information, resulting in information overload. Recommendation systems have been widely adopted, and they can help users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. To solve problems of data sparseness, enormous high‐dimensional data, the cold start problem and privacy protection in an intelligent recommender system, this study proposes a privacy‐preserving collaborative filtering recommendation method with clustering and locality‐sensitive hashing. First, we cluster users according to their characteristic information to obtain sub‐rating matrices. We use the latent factor model to predict and fill in the missing ratings in those matrices. Second, we combine the sub‐rating matrices into a complete rating matrix, subsequently, we obtained the neighbors of the target user by analyzing the similarity of the users. We use a locality‐sensitive hashing algorithm to reduce the dimensionality of the user rating data and build an index that could quickly obtain the neighbors of the target user. Finally, we predict the target user's ratings and provide recommendations to the target user. Through experiments, our study shows that our method can deal with the problems of data sparseness and cold start problems well and the accuracy of the intelligent recommendation system has been improved. In addition, we use hash techniques to search for the neighbors, which effectively protects the privacy of the user.