A graph attention network with contrastive learning for temporal review-based recommendations

In recent years, review-based recommendations have recently attracted extensive attention from researchers. However, applying reviews to improve recommendation performance still faces some problems that remain unsolved: (1) Most existing works cannot capture the time-varying user preferences and high-order collaborative features. (2) Most of the existing works often use supervised learning to train models with a limited interaction between users and items, which is not sufficient to learn the more accurate recommendation models. To overcome these problems, we propose a new Graph Attention Network with Contrastive Learning for temporary review-based recommendation, named GANCL. Specifically, to capture dynamic user preferences and high-order collaborative features, we design a user-item bipartite graph with time-series review information and ratings as its edges, and then use the graph attention and different gating mechanisms to extract the corresponding features. To make full use of the limited interaction between the users and items, we use the contrastive learning paradigm for the nodes and edges in the bipartite graph to more effectively model the user-item interaction. A large number of experiments on five public data from Amazon prove that the performance of the GANCL is improved by 2.76% and 2.83% respectively compared with the state-of-the-art algorithms in MSE and MAE.