Journal
NEUROCOMPUTING
Volume 410, Issue -, Pages 103-113Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2020.05.047
Keywords
Context-aware citation recommendation; Memory network; Bi-LSTM; Personalized author; Citation relationship
Categories
Funding
- National Key Research and Development Project [2019YFB2102500]
Ask authors/readers for more resources
The explosive growth of data leads researchers to waste time and energy to search for papers they need. Context-aware citation recommendation aims to solve this problem by analyzing a citation context and provides a list of recommended papers. In this paper, we propose a context-aware citation recommendation model based on end to end memory network. The model learns the representations of papers and citation contexts respectively based on bidirectional long short-term memory (Bi-LSTM). In particular, we jointly integrate author information and citation relationship in the distributed vector representations of citation contexts and papers. Then calculates the continuous relevance between them based on a computational multilayers memory network. We also conduct experiments on three real-world datasets to evaluate the performance of our model. (C) 2020 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available