Citation Recommendations Considering Content and Structural Context Embedding

The number of academic papers being published is increasing exponentially in recent years, and recommending adequate citations to assist researchers in writing papers is a non-trivial task. Conventional approaches may not be optimal, as the recommended papers may already be known to the users, or be...

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Bibliographic Details
Published inInternational Conference on Big Data and Smart Computing pp. 1 - 7
Main Authors Zhang, Yang, Ma, Qiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2020
Subjects
Citation Recommendation
Context modeling
Document Embedding
Hyper-document
Informatics
Information Retrieval
Link Prediction
Mathematical model
Neural networks
Prediction algorithms
Task analysis
Writing
Online AccessGet full text
ISSN2375-9356
DOI10.1109/BigComp48618.2020.0-109

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Summary:The number of academic papers being published is increasing exponentially in recent years, and recommending adequate citations to assist researchers in writing papers is a non-trivial task. Conventional approaches may not be optimal, as the recommended papers may already be known to the users, or be solely relevant to the surrounding context but not other ideas discussed in the manuscript. In this work, we propose a novel embedding algorithm DocCit2Vec, along with the new concept of "structural context", to tackle the aforementioned issues. The proposed approach demonstrates superior performances to baseline models in extensive experiments designed to simulate practical usage scenarios.
ISSN:2375-9356
DOI:10.1109/BigComp48618.2020.0-109