Deep Recommendation With Adversarial Training

Abstract

Recommender systems provide an effective solution to the information overload and have become a research hotspot in both industry and academia. Some existing work on implicit feedback has made great progress. However, there are still the following shortcomings: 1) existing pairwise ranking methods mostly sample unobserved items uniformly to obtain negative samples, which brings a biased solution and insufficient convergence; 2) the recommendation result comes from a complicated process, which makes it less interpretable. Therefore, we put forward a Deep Recommendation with Adversarial Training (DRAT) model, which provides users with personalized recommendations by utilizing an encoder-decoder structure and adversarial training. It consists of two components: one is feature learning module in which the encoder captures the text features of items from user-generated reviews, and the decoder reconstructs the text with the captured features; the other is rating prediction module which utilizes adversarial training to generate personalized negative samples tackling the drawbacks of uniform negative sampling. Extensive experiments on five real-world datasets show that our proposed model significantly outperforms the baseline methods.