In scientific social networks, group information has become an important auxiliary information to enhance the performance of paper recommendation, as many researchers prefer to obtain interested papers by joining groups. However, the existing paper recommendation methods failed to make full use of group information. In this paper, a paper recommendation method considering group information with multi-graph attention fusion network (GI-MGAF) is proposed. Specifically, in the graph construction layer, we construct researcher-paper bipartite graph, group-researcher bipartite graph and group-paper bipartite graph. In the information propagation layer, graph attention networks (GAT) are used to learn the node representations on the constructed bipartite graphs. In the information fusion layer, the researcher-level attention and paper-level attention are developed to respectively fuse the representations of researchers and papers. Experiments were conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed GI-MGAF method.


Paper Number 1385; Track Design; Short Paper



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