A Rating-Enhanced Graph Neural Network Recommendation Systems

In recent years, Graph Neural Networks (GNNs) have increasingly become a research hotspot. To further explore the application prospects of GNNs in the recommendation field, this paper conducts an in-depth study on the utilization of rating information. A rating-enhanced Graph Neural Network recommendation model is proposed. This model improves upon existing approaches by first obtaining the embedding of ratings through automatic feature engineering. Then, it utilizes a cross-attention coefficient to effectively integrate the rating embedding vectors with the embedding vectors of the two nodes, thereby obtaining the coefficients for graph spatial convolution. Experimental results show that the coefficients obtained by this model are positively correlated with the ratings, and the recommendation accuracy of graph convolution is significantly improved.