Information filtering based on corrected redundancy-eliminating mass diffusion

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user...

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Bibliographic Details
Published in:PloS one Vol. 12; no. 7; p. e0181402
Main Authors: Zhu, Xuzhen, Yang, Yujie, Chen, Guilin, Medo, Matus, Tian, Hui, Cai, Shi-Min
Format: Journal Article
Language:English
Published: United States Public Library of Science 27.07.2017
Public Library of Science (PLoS)
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ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0181402