Deep learning based dual encoder retrieval model for citation recommendation

Abstract

Citation recommendation recommends relevant documents to users based on their inputs and other information. Many traditional citation recommendation models use keywords to describe item attributes and ignore the semantics of sequences, which cause the relevance of the search results unsatisfactory. This paper proposes a deep-learning-based dual encoder retrieval (DER) model, which combines a text representation technique and a sentence pair matching approach, to improve the performance of citation recommendation. First, an input query and paper titles from publication databases are encoded to semantic vectors separately by two deep-learning-based encoders. Second, the semantic vector of the input query is matched with vectors that representing papers in the published databases by the multilayer perceptron approach to compute similarity scores. Finally, a list of documents, which are sorted in descending order of similarity scores, is generated. To validate the effectiveness of the proposed approach, it is compared with five baselines using a citation dataset. The results show that the proposed model achieves the best performance in terms of accuracy, recall, F1-measure, and AUC. In addition, we compare the DER (Glove) model with Google Scholar using a small example of twenty articles. The DER (Glove) model outperformed Google Scholar in seven recommendations, and tied in ten recommendations.