Content-Based Hybrid Deep Neural Network Citation Recommendation Method

The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.