Rating Prediction in Review-Based Recommendations via Adversarial Auto-Encoder
Yi Jin, Jiajin Huang, Qin Jin · 2018
Recommendation methods usually use users' historical ratings on items to predict ratings on their unrated items to make recommendations. However, the sparse rating data limit the recommendation quality. In order to solve the sparsity problem, other auxiliary information is combined to mine users' preferences for higher recommendation quality. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of rating and review relation of users and items. The empirical studies on real-world datasets prove that the proposed method improves recommendation performance.