AGTR: Adversarial Generation of Target Review for Rating Prediction

Recent years have witnessed a growing trend of utilizing reviews to improve the performance and interpretability of recommender systems. Almost all existing methods learn the latent representations from the user's and the item's historical reviews and then combine these two representations for rating prediction. The fatal limitation in these methods is that they are unable to utilize the most predictive review of the target user for the target item since such a review is not available at test time. In this paper, we propose a novel recommendation model, called AGTR, which can generate the unseen target review with adversarial training for rating prediction. To this end, we develop a unified framework to combine the rating tailored generative adversarial nets for synthetic review generation and the neural latent factor module using the generated target review along with historical reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks.