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Received: 2003-12-30

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.2 P.118~125


PipeCF: a DHT-based Collaborative Filtering recommendation system*

Author(s):  Rui-mi Shen, Fa Yang, Peng Han, Bo Xie

Affiliation(s):  . Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China

Corresponding email(s):   rmshen, fyang, phan, bxie@sohu.com

Key Words:  Collaborative Filtering, Distributed hash table, Significance refinement, Unanimous amplification

SHEN Rui-min, YANG Fan, HAN Peng, XIE Bo. PipeCF: a DHT-based Collaborative Filtering recommendation system[J]. Journal of Zhejiang University Science A, 2005, 6(2): 118~125.

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journal="Journal of Zhejiang University Science A",
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%T PipeCF: a DHT-based Collaborative Filtering recommendation system
%A SHEN Rui-min
%A HAN Peng
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T1 - PipeCF: a DHT-based Collaborative Filtering recommendation system
A1 - SHEN Rui-min
A1 - YANG Fan
A1 - HAN Peng
A1 - XIE Bo
J0 - Journal of Zhejiang University Science A
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EP - 125
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0118

collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalability as calculation complexity increases rapidly both in time and space when the record in the user database increases. Peer-to-peer (P2P) network has attracted much attention because of its advantage of scalability as an alternative architecture for CF systems. In this paper, authors propose a decentralized CF algorithm, called PipeCF, based on distributed hash table (DHT) method which is the most popular P2P routing algorithm because of its efficiency, scalability, and robustness. Authors also propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to improve the scalability and prediction accuracy of DHT-based CF algorithm. The experimental data show that our DHT-based CF system has better prediction accuracy, efficiency and scalability than traditional CF systems.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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