CLC number: TP391.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 2
Clicked: 7359
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.
@article{title="PipeCF: a DHT-based Collaborative Filtering recommendation system",
author="SHEN Rui-min, YANG Fan, HAN Peng, XIE Bo",
journal="Journal of Zhejiang University Science A",
volume="6",
number="2",
pages="118-125",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0118"
}
%0 Journal Article
%T PipeCF: a DHT-based Collaborative Filtering recommendation system
%A SHEN Rui-min
%A YANG Fan
%A HAN Peng
%A XIE Bo
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 2
%P 118-125
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0118
TY - JOUR
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
VL - 6
IS - 2
SP - 118
EP - 125
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0118
Abstract: 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.
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