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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| Vydáno v: | PloS one Ročník 12; číslo 7; s. e0181402 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
27.07.2017
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Cai, Shi-Min Tian, Hui Yang, Yujie Chen, Guilin Zhu, Xuzhen Medo, Matus |
| AuthorAffiliation | 3 Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P.R.China 2 Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland Shanghai University of Finance and Economics, CHINA 4 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, P.R.China 1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China |
| AuthorAffiliation_xml | – name: 4 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, P.R.China – name: 1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China – name: 2 Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland – name: 3 Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P.R.China – name: Shanghai University of Finance and Economics, CHINA |
| Author_xml | – sequence: 1 givenname: Xuzhen surname: Zhu fullname: Zhu, Xuzhen – sequence: 2 givenname: Yujie surname: Yang fullname: Yang, Yujie – sequence: 3 givenname: Guilin surname: Chen fullname: Chen, Guilin – sequence: 4 givenname: Matus surname: Medo fullname: Medo, Matus – sequence: 5 givenname: Hui surname: Tian fullname: Tian, Hui – sequence: 6 givenname: Shi-Min orcidid: 0000-0002-2089-5150 surname: Cai fullname: Cai, Shi-Min |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28749976$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s12525_021_00488_x crossref_primary_10_1080_15332861_2024_2375966 crossref_primary_10_1016_j_physa_2019_122073 |
| Cites_doi | 10.1145/963770.963772 10.1109/TKDE.2005.99 10.1109/TSC.2014.2355842 10.1007/s11257-012-9136-x 10.1209/0295-5075/82/58007 10.1007/s11704-013-3012-7 10.1109/MIC.2003.1167344 10.1103/PhysRevLett.87.248701 10.1073/pnas.1000488107 10.1103/PhysRevE.84.037101 10.1145/1864708.1864746 10.1103/PhysRevE.76.046115 10.1209/0295-5075/111/48007 10.1209/0295-5075/80/68003 10.1145/1060745.1060754 10.1103/PhysRevLett.99.154301 10.1016/j.knosys.2013.11.006 10.1088/1367-2630/11/12/123008 10.1016/S1389-1286(00)00083-9 10.1016/j.physrep.2012.02.006 10.1023/A:1021240730564 10.1023/A:1011419012209 10.1145/2792838.2796546 10.1109/TKDE.2013.168 10.1145/1924421.1924442 10.1145/1014052.1014097 10.1007/s11390-011-0176-1 10.1088/1367-2630/10/12/123027 10.1209/0295-5075/81/58004 10.1145/1409540.1409544 10.1103/PhysRevE.83.066119 10.1088/1742-5468/2014/07/P07004 |
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| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Zhu et al 2017 Zhu et al |
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| SubjectTerms | Accuracy Algorithms Area Under Curve Automatic classification Automatic indexing Biology and Life Sciences Collaboration Communications networks Computer and Information Sciences Computer Communication Networks Customers Databases as Topic Diffusion Electronic commerce Filtration Methods Physical Sciences Recommender systems Redundancy Research and Analysis Methods Similarity Social Sciences Sparsity |
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| Title | Information filtering based on corrected redundancy-eliminating mass diffusion |
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