Citation Recommendation Based on Knowledge Graph and Multi-task Learning

The Citation Recommendation aims to address the problem of academic information overload by filtering and suggesting relevant references for researchers. Traditional content-based citation recommendation methods may not be comprehensive enough to extract paper attributes that are essential for evaluating paper content similarity. To better use the abundant attributes and interaction information, the knowledge graph is introduced to recommendation system recently. We construct a multi-task learning-based model for citation recommendation that incorporates a knowledge graph, consisting of two primary tasks: citation recommendation and knowledge graph link prediction. To identify the interactions between papers, we propose a pseudointeraction matrix in the citation recommendation task. The knowledge graph link prediction task aids in identifying paper attribute information and enhancing representation. By automatically merging and sharing low-level features, exploring feature similarity, and enhancing the performance of both tasks, the multi-task learning framework can improve the final recommendation result significantly. Multiple experiments on the academic paper datasets AMiner and DBLP verify the effectiveness of our proposed model.