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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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. |
format | Online Article Text |
id | pubmed-4163396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT dengkun mobilerecommendationbasedonlinkcommunitydetection AT zhangjianpei mobilerecommendationbasedonlinkcommunitydetection AT yangjing mobilerecommendationbasedonlinkcommunitydetection |