The Multi-Behavior Graph Contrastive Learning Recommendation Method with Self-Attention Mechanism
Graph convolutional network has been widely applied in multi-behavior recommender systems due to its powerful ability to learn high-order collaborative signal.However,most existing graph convolution-based multi-behavior recommendation methods have failed to effectively model the relationships between different user-item nodes and various behaviors.The sparsity of target behaviors also poses challenges to further improve the performance of multi-behavior rec-ommendation algorithms.Based on this,we propose the multi-behavior graph contrastive learning recommendation model with self-attention mechanism(SA-MBGCL).This method combines user-item node embeddings with behavior embed-dings and employs a self-attention mechanism to enhance embedding representations,effectively modeling the dependency relationships between different nodes and behaviors.In the meanwhile,a graph contrastive learning approach is constructed,treating the target behavior and auxiliary behaviors of the same user as positive pairs,while considering those of different users as negative pairs,thereby reinforcing behavioral differences among different users to alleviate the sparsity of target be-haviors.The proposed model combines unsampled recommendation tasks with multi-behavior graph contrastive learning to perform multi-task joint optimization.It was compared with 6 single-behavior models and 10 multi-behavior models on two public datasets,Beibei and Taobao.The results show that the proposed model SA-MBGCL achieves an average improve-ment of 5.21%in Hit Ratio(HR)and 8.30%in Normalized Discounted Cumulative Gain(NDCG).This demonstrates the ef-fectiveness of the method presented in this work.
self-attention mechanismgraph contrastive learninggraph convolutional networkmulti-taskmulti-be-haviorrecommender system
万方数据
维普
