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Claper: Recommend classical papers to beginners

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4 Author(s)
Yonggang Wang ; Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing, China ; Ennan Zhai ; Jianbin Hu ; Zhong Chen

Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas.

Published in:
Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on  (Volume:6 )

Date of Conference: 10-12 Aug. 2010

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