Hyperboloid-Aware Cross-Community Knowledge Graph Contrastive Learning for Paper Recommendation

With the rapid development of scientific research, a large amount of literature materials (e.g., published papers) has brought a serious information overload problem to researchers. For those novices who have just stepped into a certain research field, the fact that they do not yet know their own research direction, coupled with the huge amount of literature materials and their varying quality, makes it even more difficult for them to retrieve high-quality papers. To this end, we propose a Cross-community academic Knowledge Graph based approach for machine learning paper Recommendation (CKGR). Considering the hierarchical structure of cross-community knowledge graphs, we utilize knowledge propagation in hyperbolic space for entity representation learning. To alleviate the data sparsity problem as well as to learn better entity representations, we further introduce a preference migration module and contrastive learning. Meanwhile, considering the semantic relationships among entities, we introduce textual information to enhance the connection among interacting nodes for better recommendation tasks.