Paper Info
Title
Collaborative Filtering With Network Representation Learning for Citation Recommendation
Abstract
Citation recommendation plays an important role in the context of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is challenging due to the cold start problem and the lack of paper ratings. To address these challenges, in this article, we propose a collaborative filtering with network representation learning framework for citation recommendation, namely CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and MRR (Mean Reciprocal Rank). Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines.
Year
DOI
Venue
2022
10.1109/TBDATA.2020.3034976
IEEE Transactions on Big Data
Keywords
DocType
Volume
Network representation learning,collaborative filtering,citation recommendation,scholarly big data
Journal
8
Issue
ISSN
Citations 
5
2332-7790
1
PageRank 
References 
Authors
0.35
36
6
Name
Order
Citations
PageRank
Wei Wang17122746.33
Tao Tang266478.90
Feng Xia32013153.69
Zhiguo Gong472667.16
Zhikui Chen569266.76
Huan Liu612695741.34