Adversarial Training for Review-Based Recommendations
Dimitrios Rafailidis, Fábio Crestani · 2019
Recent studies have shown that incorporating users' reviews into the collaborative filtering strategy can significantly boost the recommendation accuracy. A pressing challenge resides on learning how reviews influence users' rating behaviors. In this paper, we propose an Adversarial Training approach for Review-based recommendations, namely ATR. We design a neural architecture of sequence-to-sequence learning to calculate the deep representations of users' reviews on items following an adversarial training strategy. At the same time we jointly learn to factorize the rating matrix, by regularizing the deep representations of reviews with the user and item latent features. In doing so, our model captures the non-linear associations among reviews and ratings while producing a review for each user-item pair. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed model, outperforming other state-of-the-art methods.