Hybrid high-order semantic graph representation learning for recommendations

Abstract

AbstractThe amount of Internet data is increasing day by day with the rapid development of information technology. To process massive amounts of data and solve information overload, researchers proposed recommender systems. Traditional recommendation methods are mainly based on collaborative filtering algorithms, which have data sparsity problems. At present, most model-based collaborative filtering recommendation algorithms can only capture first-order semantic information and cannot process high-order semantic information. To solve the above issues, in this paper, we propose a hybrid graph neural network model based on heterogeneous graphs with high-order semantic information extraction, which models users and items respectively by learning low-dimensional representations for them. We introduced an attention mechanism to allow the model to understand the corresponding edge weights adaptively. Simultaneously, the model also integrates social information in the data to learn more abundant information. We performed some experiments on related datasets. Our method achieved better results than some current advanced models, which verified the proposed model’s effectiveness.