Enhancing Interactive Graph Representation Learning for Review-based Item Recommendation

Guojiang Shen, Jiajia Tan, Zhi Liu, Xiangjie Kong

Collaborative filtering has been successful in the recommendation systems of various scenarios, but it is also hampered by issues such as cold start and data sparsity. To alleviate the above problems, recent studies have attempted to integrate review information into models to improve accuracy of rating prediction. While most of the existing models respectively utilize independent module to ex-tract the latent feature representation of user reviews and item reviews, ignoring the correlation between the latent features, which may fail to capture the similarity of user preferences and item attributes hidden in different review text. On the other hand, the graph neural network can realize the information interaction in high dimensional space through deep architecture, which has been extensively studied in many fields. Therefore, in order to explore the high dimensional relevance between users and items hidden in the review information, we propose a new recommendation model enhancing interactive graph representation learning for review-based item recommendation, named IGRec. Specifically, we construct the user-review-item graph with users/items as nodes and reviews as edges. We further add the connection of the user-user and the item-item to the graph by meta-path of user-item-user and item-user-item. Then we utilize the attention mechanism to fuse edges information into nodes and apply the multilayer graph convolutional network to learn the high-order interactive information of nodes. Finally, we obtain the final embedding of user/item and adopt the factorization machine to complete the rating prediction. Experiments on the five real-world datasets demonstrate that the proposed IGRec outperforms the state-of-the-art baselines.