Group Recommendation Algorithms Incorporating Hypergraph Convolution and Self-supervised Collaborative Training
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