标题:Citation recommendation with a content-sensitive deepwalk based approach
作者:Guo, Lantian ;Cai, Xiaoyan ;Qin, Haohua ;Guo, Yangming ;Li, Fei ;Tian, Gang
作者机构:[Guo, Lantian ;Guo, Yangming ] School of Computer Science, Northwestern Polytechnical University, Xi'an; 710072, China;[Li, Fei ] School of Informatio 更多
会议名称:19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
会议日期:8 November 2019 through 11 November 2019
来源:IEEE International Conference on Data Mining Workshops, ICDMW
出版年:2019
卷:2019-November
页码:538-543
DOI:10.1109/ICDMW.2019.00082
关键词:Citation recommendation; Content information; DeepWalk; Network structure
摘要: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). © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078782958&doi=10.1109%2fICDMW.2019.00082&partnerID=40&md5=78dae135a90535a153d4c875b06b3eda
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