Utilizing Generative Adversarial Networks for Recommendation based on Ratings and Reviews

Abstract

Many existing rating-based recommendation algorithms have achieved relative success. However, the real-world datasets are extremely sparse and most rating-based algorithms are still suffering from the data sparsity problem. Along with integer-valued ratings, we consider that the user-generated review is also an important user feedback. Furthermore, compared with the traditional recommendation algorithms which have the limited ability to learn the distributions of ratings and reviews simultaneously, the generative adversarial networks can learn better representations for data. In this paper, we propose Rating and Review Generative Adversarial Networks (RRGAN), an innovative framework for recommendation, in which the generative model and discriminative model play a minimax game. Specifically, the generative model predicts the ratings of top-N list for users or items based on reviews, while the discriminative model aims to distinguish the predicted ratings from real ratings. With the competition between these two models, RRGAN improves the ability of understanding users and items based on ratings and reviews. We introduce the user profiles, item representations and ratings into a matrix factorization model to predict the top-N list for the users. In addition, we study three different architectures to learn reasonable user profiles and item representations based on ratings and reviews to achieve better recommendations. To evaluate the performance of our model, we conduct the extensive experiments on three real-world amazon datasets in three parts, which are top-N recommendation analysis, case study and long-tail users analysis. The experimental results show that our method significantly outperforms various state-of-the-art methods, including LFM, LambdaFM, HFT, DeepCoNN and IRGAN methods.