Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation

Abstract

Textual reviews contain rich semantic information that is useful for making better recommendation, as such semantic information may indicate more fine-grained preferences of users. Recent efforts make considerable improvement on recommendation by integrating textual reviews in rating-based recommendations. However, there still exist major challenges on integrating textual reviews for recommendation. On the one hand, most existing works focus on learning a single representation from reviews but ignoring complex relations between users (or items) and reviews, which may fail to capture user preferences and item attributes together. On the other hand, these works independently learn latent representations from ratings and reviews while omitting correlations between rating-based features and review-based features, which may harm recommendation performance. In this article, we capture the aspect-aware relations by constructing heterogeneous graphs from reviews. Furthermore, we propose a new recommendation model, namely AHOR, to jointly distill rating-based features and review-based features, which are derived from ratings and reviews, respectively. To explore the multi-hop connectivity information between users, items, and aspects, a novel graph neural network is introduced to learn aspect-aware high-order representations. Experiments based on public datasets show that our approach outperforms state-of-the-art methods. We also provide detailed analysis on the high-order signals and the aspect importance to show the interpretability of our proposed model.