Citation Recommendations Considering Content and Structural Context Embedding

• Published in: International Conference on Big Data and Smart Computing pp. 1 - 7
• Main Authors: Zhang, Yang, Ma, Qiang
• Format: Conference Proceeding
• Language: English
• 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
• ISSN: 2375-9356
• DOI: 10.1109/BigComp48618.2020.0-109

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.