Abstract: Addressing the issue that existing citation recommendation methods primarily focus on modeling binary relationships using graph structures, and lack sufficient representation of the diversity and variety of node types and interaction relationships, a citation recommendation method based on dual-channel heterogeneous hypergraph neural networks is proposed. Firstly, a heterogeneous graph is constructed, convolutional neural networks (CNNs) and Transformers are utilized to encode the local and global semantic features of each node in the heterogeneous graph, respectively, obtaining structural representations of the target node on the heterogeneous graph channel. Secondly, multiple types of hyperedges are designed to expand heterogeneous data information. Thirdly, a hypergraph is used to encode interactions between nodes, and a hypergraph neural network is employed to capture potential complex high-order semantic relationships in the hypergraph, obtaining semantic representations of the target node on the hypergraph channel. Finally, information from the two channels is aggregated to obtain the final semantic representation of the target node. The correlation between the target paper node and candidate paper nodes is calculated to generate a citation recommendation list. Experimental results on the DBLP and PubMed datasets demonstrate that the proposed method can effectively improve the quality of citation recommendations and achieve better recommendation outcomes.

Key words: citation recommendation, heterogeneous graph, hypergraph neural network, information fusion