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Title: Paper Recommendation Based on Citation Relation
Searching for relevant literature is a fundamental part of academic research. The search for relevant literature is becoming a more difficult and time-consuming task as millions of articles are published each year. As a solution, recommendation systems for academic papers attempt to help researchers find relevant papers quickly. This paper focuses on graph-based recommendation systems for academic papers using citation networks. This type of paper recommendation system leverages a graph of papers linked by citations to create a list of relevant papers. In this study, we explore recommendation systems for academic papers using citation networks incorporating citation relations. We define citation relation based on the number of times the origin paper cites the reference paper, and use this citation relation to measure the strength of the relation between the papers. We created a weighted network using citation relation as citation weight on edges. We evaluate our proposed method on a real-world publication data set, and conduct an extensive comparison with three state-of-the-art baseline methods. Our results show that citation network-based recommendation systems using citation weights perform better than the current methods.  more » « less
Award ID(s):
1659645
PAR ID:
10173023
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE International Conference on Big Data
ISSN:
2639-1589
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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