UniDNR: A Novel Interaction Graph and Texts Fusion Method for Review-based Recommendation

Review-based recommender systems aim to calculate users' preference for items by leveraging user reviews. Current methods mainly consist of two components: user and item embedding learning and user-item rating predicting. But these methods overlook the higher-order interaction relationships in the user-item graph which are beneficial to capture users’ preferences and features among items. Also, these methods overlook the inherent attributes in item descriptions which complement user reviews. In this paper, we propose a deep neural recommendation framework named UniDNR that unites item descriptions, user reviews and the user-item interaction graph to make recommendations. UniDNR can be divided into three parts: the ID-level embedding layer, the text-level embedding layer and the rating prediction layer. Specifically, the ID-level embedding layer captures the higher-order interactive relationship in the user-item interaction graph which can better share features among users and items. The text-level embedding layer focuses on embedding items and users by aspect-based learning which considering different aspects mentioned in descriptions and reviews. Such that, we combine ID embedding and text embedding to predict the most likely final rating assigned by the user. Experiments on three real-world datasets demonstrate the superiority of our proposed UniDNR model compared to the state-of-the-art baselines.