CAR: Incorporating filtered citation relations for scientific article recommendation
Date
2015
Authors
Liu, H.
Yang, Z.
Lee, I.
Xu, Z.
Yu, S.
Xia, F.
Editors
Liu, X.
Hsu, R.
Wang, P.
Xia, F.
Wang, Y.
Dong, M.
Deng, Y.
Hsu, R.
Wang, P.
Xia, F.
Wang, Y.
Dong, M.
Deng, Y.
Advisors
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Conference paper
Citation
Proceedings 2015 IEEE International Conference on Smart City Smartcity 2015 Held Jointly with 8th IEEE International Conference on Social Computing and Networking Socialcom 2015 5th IEEE International Conference on Sustainable Computing and Communications Sustaincom 2015 2015 International Conference on Big Data Intelligence and Computing Datacom 2015 5th International Symposium on Cloud and Service Computing Sc2 2015, 2015 / Liu, X., Hsu, R., Wang, P., Xia, F., Wang, Y., Dong, M., Deng, Y. (ed./s), pp.513-518
Statement of Responsibility
Conference Name
2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) (19 Dec 2015 - 21 Dec 2015 : PEOPLES R CHINA, Chengdu)
Abstract
With the rapid proliferation of information technology, researchers have access to large archives of scientific articles. This makes it more challenging to find articles of interest for researchers. Consequently, a solution to this problem, scientific article recommendation, has become a hot research topic in recent years. In this paper, we propose a novel article recommendation method called Citation-based scientific Article Recommendation (CAR). CAR combines the information of researchers' historical preferences and citation relations between articles. We take into account the fact that, not all pairwise articles with citation relations are highly relevant although researchers generally find articles of interest by searching citations. Therefore, in our proposed method, weak citation relations are first filtered out through an association mining technique using data on researchers' historical preferences. Then, these filtered citation relations are incorporated into a graph-based article ranking method for enhancing recommendation quality. Through a relevant real-world dataset, we evaluate our proposed method. Our experimental results verify that the proposed method significantly outperforms other existing baseline methods in terms of precision, recall, and F1.
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Copyright 2015 IEEE