article

DCFLA: A distributed collaborative-filtering neighbor-locating algorithm

Authors:

Zheng ChenAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 177, Issue 6

Pages 1349 - 1363

https://doi.org/10.1016/j.ins.2006.09.005

Published: 20 March 2007 Publication History

Abstract

Although collaborative filtering (CF) has proved to be one of the most successful techniques in recommendation systems, it suffers from a lack of scalability as the time complexity rapidly increases when the number of the records in the user database increases. As a result, distributed collaborative filtering (DCF) is attracting increasing attention as an alternative implementation scheme for CF-based recommendation systems. In this paper, we first propose a distributed user-profile management scheme using distributed hash table (DHT)-based routing algorithms, which is one of the most popular and effective approaches in peer-to-peer (P2P) overlay networks. In this DCF scheme, an efficient DCF neighbor-locating algorithm (DCFLA) is proposed, together with two improvements, most same opinion (MSO) and average rating normalization (ARN), to reduce the network traffic and time cost. Finally, we analyze the performance of one baseline and three novel CF algorithms are being proposed: (1) a traditional memory-based CF (baseline); (2) a basic DHT-based CF; (3) a DHT-based CF with MSO; and (4) a DHT-based CF with MSO and ARN. The experimental results show that the scalability of our proposed DCFLA is much better than the traditional centralized CF algorithm and the prediction accuracies of these two systems are comparable.

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Cited By

Zou BLi CTan LChen H(2019)GPUTENSORInformation Sciences: an International Journal10.1016/j.ins.2014.12.004299:C(159-177)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2014.12.004
Ahn H(2019)A new similarity measure for collaborative filtering to alleviate the new user cold-starting problemInformation Sciences: an International Journal10.1016/j.ins.2007.07.024178:1(37-51)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2007.07.024
Dooms SAudenaert PFostier JDe Pessemier TMartens L(2018)In-memory, distributed content-based recommender systemJournal of Intelligent Information Systems10.1007/s10844-013-0276-142:3(645-669)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-013-0276-1
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Index Terms

DCFLA: A distributed collaborative-filtering neighbor-locating algorithm
1. Human-centered computing
  1. Collaborative and social computing
2. Information systems

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Copyright © Elsevier Inc. © 2006.

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Publication History

Published: 20 March 2007

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Cited By

Zou BLi CTan LChen H(2019)GPUTENSORInformation Sciences: an International Journal10.1016/j.ins.2014.12.004299:C(159-177)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2014.12.004
Ahn H(2019)A new similarity measure for collaborative filtering to alleviate the new user cold-starting problemInformation Sciences: an International Journal10.1016/j.ins.2007.07.024178:1(37-51)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2007.07.024
Dooms SAudenaert PFostier JDe Pessemier TMartens L(2018)In-memory, distributed content-based recommender systemJournal of Intelligent Information Systems10.1007/s10844-013-0276-142:3(645-669)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-013-0276-1
Zaouali KHaddad MZghal H(2018)Distributed Collaborative Filtering for Batch and Stream Processing-Based RecommendationsOn the Move to Meaningful Internet Systems. OTM 2018 Conferences10.1007/978-3-030-02610-3_14(243-260)Online publication date: 22-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-02610-3_14
Karydi EMargaritis K(2016)Parallel and Distributed Collaborative FilteringACM Computing Surveys10.1145/295195249:2(1-41)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2951952
Kaleli CPolat H(2015)Privacy-Preserving Naïve Bayesian Classifier-Based Recommendations on Distributed DataComputational Intelligence10.1111/coin.1201231:1(47-68)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1111/coin.12012
Knees PSchnitzer DFlexer AKankanhalli MRueger SManmatha RJose Jvan Rijsbergen K(2014)Improving Neighborhood-Based Collaborative Filtering by Reducing HubnessProceedings of International Conference on Multimedia Retrieval10.1145/2578726.2578747(161-168)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1145/2578726.2578747
Lee HKim J(2012)Mining knowledge demands from information flowExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.12.04539:8(6799-6806)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.12.045
Kim JLee H(2012)Extraction of user profile based on workflow and information flowExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.11.07539:5(5478-5487)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.11.075
Zhao YFeng XLi JLiu BMobasher BBurke RJannach DAdomavicius G(2011)Shared collaborative filteringProceedings of the fifth ACM conference on Recommender systems10.1145/2043932.2043942(29-36)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1145/2043932.2043942
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