Deep learning based dual encoder retrieval model for citation recommendation

Fang Da, Gang Kou and Yi Peng

Technological Forecasting and Social Change, 2022, vol. 177, issue C

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.

Keywords: Deep learning retrieval approach; Citation recommendation; Dual encoder retrieval model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162522000774
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000774

DOI: 10.1016/j.techfore.2022.121545

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().