TY - GEN
T1 - Citation recommendation with a content-sensitive deepwalk based approach
AU - Guo, Lantian
AU - Cai, Xiaoyan
AU - Qin, Haohua
AU - Guo, Yangming
AU - Li, Fei
AU - Tian, Gang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Systems for recommending scientific papers mainly help researchers to find a list of references that related to the researcher's interest effectively and automatically. Many state-of-the-art technique have been used for recommendation system, however, the traditional approaches has the issues of data scarcities and cold start, and existing recommended approaches with network representation only focus on one aspect of node information and cannot leverage content information. In this paper, we proposed a Citation Recommendation method with a Content-Aware bibliographic network representation, called CR-CA, whose recommended process contains two levels: (1) At the node content level, the proposed approach calculates similarities between the target and candidate papers, selecting an initial seed set of papers; (2) At the citation network structure level, this approach exploits citation relationship between papers to study latent representation of the scientific papers based on a deep natural language method-DeepWalk. The proposed approach was tested on the AAN dataset demonstrate that this approach outperforms baseline algorithms, in the true positive rate (Recall) and normalized discounted cumulative gain (NDCG).
AB - Systems for recommending scientific papers mainly help researchers to find a list of references that related to the researcher's interest effectively and automatically. Many state-of-the-art technique have been used for recommendation system, however, the traditional approaches has the issues of data scarcities and cold start, and existing recommended approaches with network representation only focus on one aspect of node information and cannot leverage content information. In this paper, we proposed a Citation Recommendation method with a Content-Aware bibliographic network representation, called CR-CA, whose recommended process contains two levels: (1) At the node content level, the proposed approach calculates similarities between the target and candidate papers, selecting an initial seed set of papers; (2) At the citation network structure level, this approach exploits citation relationship between papers to study latent representation of the scientific papers based on a deep natural language method-DeepWalk. The proposed approach was tested on the AAN dataset demonstrate that this approach outperforms baseline algorithms, in the true positive rate (Recall) and normalized discounted cumulative gain (NDCG).
KW - Citation recommendation
KW - Content information
KW - DeepWalk
KW - Network structure
UR - http://www.scopus.com/inward/record.url?scp=85078782958&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2019.00082
DO - 10.1109/ICDMW.2019.00082
M3 - 会议稿件
AN - SCOPUS:85078782958
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 538
EP - 543
BT - Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
A2 - Papapetrou, Panagiotis
A2 - Cheng, Xueqi
A2 - He, Qing
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Y2 - 8 November 2019 through 11 November 2019
ER -