A Local Differential Privacy based Hybrid Recommendation Model with BERT and Matrix Factorization

Many works have proposed integrating sentiment analysis with collaborative filtering algorithms to improve the accuracy of recommendation systems. As a result, service providers collect both reviews and ratings, which is increasingly causing privacy concerns among users. Several works have used the Local Differential Privacy (LDP) based input perturbation mechanism to address privacy concerns related to the aggregation of ratings. However, researchers have failed to address whether perturbing just ratings can protect the privacy of users when both reviews and ratings are collected. We answer this question in this paper by applying an LDP based perturbation mechanism in a recommendation system that integrates collaborative filtering with a sentiment analysis model. On the user-side, we use the Bounded Laplace mechanism (BLP) as the input rating perturbation method and Bidirectional Encoder Representations from Transformers (BERT) to tokenize the reviews. At the service provider's side, we use Matrix Factorization (MF) with Mixture of Gaussian (MoG) as our collaborative filtering algorithm and Convolutional Neural Network (CNN) as the sentiment classification model. We demonstrate that our proposed recommendation system model produces adequate recommendation accuracy under strong privacy protection using Amazon's review and rating datasets.