Rating-Aware Homogeneous Review Graphs and User Likes/Dislikes Differentiation for Effective Recommendations

The goal of Review-Based Recommendation System (RBRS) is to effectively learn the representations of users and items by utilizing review texts in addition to user-item interactions. From user-item interaction graphs widely employed in recommendation systems, recent RBRS methods using graph neural networks (GNNs) obtain the representations by associating each edge between a user and an item with the review information of the user for that item. However, these GNN-based RBRS methods present two main issues: (1) by con- verting each review text into the weight, i.e., single value, of a edge between a user node and an item node, they lose the rich informa- tion about users and items inherent in the review; and (2) by creating only a single general representation for each user, they cannot repre- sent the individual effects of users' likes and dislikes on their ratings for items they have interacted with. To address these problems, we propose a novel GNN-based RBRS, named LETTER, utilizing homo- geneous graphs, i.e., user-user graphs and an item-item graph, to learn general representations of users and items along with users' like and dislike representations. LETTER can learn user and item representations without losing review information by utilizing the proposed homogeneous graphs. Furthermore, LETTER explicitly designs the influence of users' like and dislike representations on their ratings to perform accurate rating predictions. Through ex- periments on six datasets, we verify that the proposed LETTER out- performs nine state-of-the-art RBRSs by up to 23.1%. Our source code is available at https://github.com/Bigdasgit/LETTER.