With the popularity of social media,people are gradually shifting their research direction from individual recommendation algorithms to group recommendation algorithms.Most existing group recommendation models use heuristic or attention-based preference aggregation strategies to aggregate the individual preferences of group mem-bers to form group preferences.To further improve this task,this paper presents a group recommendation algorithm(HCSC)that incorporates hypergraph convolution and self-supervised collaborative training.First,in the user-level hypergraph,three channels are used to encode higher-order user relationships in the hypergraph convolu-tional network to enhance user representation by aggregating user features learned from multiple channels.Secondly,in group-level hypergraphs,all groups are connected as overlapping networks and the attention is paid to the individual preferences of common group members,in which the process of hyperedge embedding can be consid-ered as the learning of group preferences.Thirdly,to further enhance the cluster representation,self-supervised learning and co-training are combined to construct two different graph encoders on the above two hypergraphs,re-cursively using annotated samples generated from different information to supervise each other through comparative learning,and the proposed self-supervised co-training retains complete information and achieves true data enhance-ment compared to the discard strategy.Experiments on two real-world datasets demonstrate the superiority of the proposed HCSC model in this paper.
group recommendationhypergraph convolutionself-supervised learningco-trainingcontrast learning
维普
万方数据
