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New Technology of Library and Information Service  2011, Vol. Issue (11): 44-47    DOI: 10.11925/infotech.1003-3513.2011.11.07
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Research of Personalized Book Recommender System of University Library Based on Collaborative Filter
Dong Kun
Library of South-Central University for Nationalities, Wuhan 430074, China
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Abstract  Aiming at the disadvantages of insufficient mining and analysis of readers' information needs existing in the active book recommendation service of university library, the paper brings forward a construction of personalized book recommender system based on collaborative filter. The system imports the factors of faculty, role, education and the readers' records of visiting the reading rooms to construct the reader's characteristic model. By mining and analyzing the characteristic model which uses optimized collaboration filter algorithm, the system can produce the personalized book recommendation to reader.And the experiment proves that the system is efficient and practical.
Key wordsCollaborative filter      Information service      Data mining      Data warehouse     
Received: 01 July 2011      Published: 06 January 2012
:  G205.7  

Cite this article:

Dong Kun. Research of Personalized Book Recommender System of University Library Based on Collaborative Filter. New Technology of Library and Information Service, 2011, (11): 44-47.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2011.11.07     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2011/V/I11/44

[1] 熊拥军,陈春颖.基于关联挖掘技术的数字图书馆个性化推送服务[J]. 图书情报工作, 2010,54(1):125-129.
[2] 曹美琴.数据挖掘在图书馆个性化服务中的应用研究 .西安:西北大学,2008.
[3] Bresse J S,Heckerman D,Kadie C.Empirical Analysis of Predictive Algorithms for Collaborative Filtering .In:Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco:Morgan Kaufmann,1998:43-52.
[4] Agrawal R,Imielinski T,Swami A. Mining Associations Between Sets of Items in Large Databases .In:Proceedings of the ACM SIGMOD International Conference on Management of Date. 1993:207-216.
[5] 邵伟.基于领域知识的协同过滤推荐研究 .保定:河北大学,2010.
[6] 常富洋,林鸿飞,许侃.基于用户向量扩展的协同推荐方法[J]. 情报学报, 2010,29(4):688-694.
[7] Sarwar B,Karypis G,Konstan J,et al.Item-based Collaborative Filtering Recommendation Algorithms .In:Proceedings of the 10th International World Wide Web Conference. New York:ACM Press,2001:285-295.
[8] 柯平,高洁.信息管理概论[M].北京:科学出版社,2007:205-207.
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