Multi-behavior Recommender Model Based on LightGCN

Graph convolutional networks have gained traction in recommender systems recently, addressing issues like matrix sparsity. LightGCN simplifies models to avoid overfitting and improve generalization. However, it only considers single behavior, neglecting the impact of multiple behaviors on user preferences. Hence, we propose a multi-behavior recommender based on lightweight graph convolution. We construct a heterogeneous graph capturing various user-item interactions and design a heterogeneous graph attention network. User embeddings from the graph neural network are mapped to different behaviors, enhancing user information mining. Multi-task training enhances model performance, as evidenced by superior results compared to LightGCN and NGCF across multiple datasets.