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.
%0 Conference Paper
%1 wang2010claper
%A Wang, Yonggang
%A Zhai, Ennan
%A Hu, Jianbin
%A Chen, Zhong
%B Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery
%D 2010
%I IEEE
%K analysis citation item paper recommender research
%P 2777--2781
%R 10.1109/FSKD.2010.5569227
%T Claper: Recommend classical papers to beginners
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227
%V 6
%X 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.
@inproceedings{wang2010claper,
abstract = {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.},
added-at = {2012-03-13T08:58:56.000+0100},
author = {Wang, Yonggang and Zhai, Ennan and Hu, Jianbin and Chen, Zhong},
biburl = {https://www.bibsonomy.org/bibtex/27da72bf2f0538afad9377a0d50c263b4/jaeschke},
booktitle = {Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery},
doi = {10.1109/FSKD.2010.5569227},
interhash = {7180ddaf1c1765a45fd244027bd0bf43},
intrahash = {7da72bf2f0538afad9377a0d50c263b4},
keywords = {analysis citation item paper recommender research},
month = aug,
pages = {2777--2781},
publisher = {IEEE},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Claper: Recommend classical papers to beginners},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227},
volume = 6,
year = 2010
}