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Mobile Recommendation Based on Link Community Detection

Since traditional mobile recommendation systems have difficulty in acquiring complete and accurate user information in mobile networks, the accuracy of recommendation is not high. In order to solve this problem, this paper proposes a novel mobile recommendation algorithm based on link community dete...

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Detalles Bibliográficos
Autores principales: Deng, Kun, Zhang, Jianpei, Yang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4163396/
https://www.ncbi.nlm.nih.gov/pubmed/25243204
http://dx.doi.org/10.1155/2014/259156
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author Deng, Kun
Zhang, Jianpei
Yang, Jing
author_facet Deng, Kun
Zhang, Jianpei
Yang, Jing
author_sort Deng, Kun
collection PubMed
description Since traditional mobile recommendation systems have difficulty in acquiring complete and accurate user information in mobile networks, the accuracy of recommendation is not high. In order to solve this problem, this paper proposes a novel mobile recommendation algorithm based on link community detection (MRLD). MRLD executes link label diffusion algorithm and maximal extended modularity (EQ) of greedy search to obtain the link community structure, and overlapping nodes belonging analysis (ONBA) is adopted to adjust the overlapping nodes in order to get the more accurate community structure. MRLD is tested on both synthetic and real-world networks, and the experimental results show that our approach is valid and feasible.
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spelling pubmed-41633962014-09-21 Mobile Recommendation Based on Link Community Detection Deng, Kun Zhang, Jianpei Yang, Jing ScientificWorldJournal Research Article Since traditional mobile recommendation systems have difficulty in acquiring complete and accurate user information in mobile networks, the accuracy of recommendation is not high. In order to solve this problem, this paper proposes a novel mobile recommendation algorithm based on link community detection (MRLD). MRLD executes link label diffusion algorithm and maximal extended modularity (EQ) of greedy search to obtain the link community structure, and overlapping nodes belonging analysis (ONBA) is adopted to adjust the overlapping nodes in order to get the more accurate community structure. MRLD is tested on both synthetic and real-world networks, and the experimental results show that our approach is valid and feasible. Hindawi Publishing Corporation 2014 2014-08-26 /pmc/articles/PMC4163396/ /pubmed/25243204 http://dx.doi.org/10.1155/2014/259156 Text en Copyright © 2014 Kun Deng et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Deng, Kun
Zhang, Jianpei
Yang, Jing
Mobile Recommendation Based on Link Community Detection
title Mobile Recommendation Based on Link Community Detection
title_full Mobile Recommendation Based on Link Community Detection
title_fullStr Mobile Recommendation Based on Link Community Detection
title_full_unstemmed Mobile Recommendation Based on Link Community Detection
title_short Mobile Recommendation Based on Link Community Detection
title_sort mobile recommendation based on link community detection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4163396/
https://www.ncbi.nlm.nih.gov/pubmed/25243204
http://dx.doi.org/10.1155/2014/259156
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AT yangjing mobilerecommendationbasedonlinkcommunitydetection