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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/259156 |
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| _version_ | 1850216881238048768 |
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| author | Kun Deng Jianpei Zhang Jing Yang |
| author_facet | Kun Deng Jianpei Zhang Jing Yang |
| author_sort | Kun Deng |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-2f7c74b1f0ef4d9aa6c4d76bc2493499 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2f7c74b1f0ef4d9aa6c4d76bc24934992025-08-20T02:08:12ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/259156259156Mobile Recommendation Based on Link Community DetectionKun Deng0Jianpei Zhang1Jing Yang2College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin 150001, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin 150001, ChinaSince 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.http://dx.doi.org/10.1155/2014/259156 |
| spellingShingle | Kun Deng Jianpei Zhang Jing Yang Mobile Recommendation Based on Link Community Detection The Scientific World Journal |
| 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 |
| url | http://dx.doi.org/10.1155/2014/259156 |
| work_keys_str_mv | AT kundeng mobilerecommendationbasedonlinkcommunitydetection AT jianpeizhang mobilerecommendationbasedonlinkcommunitydetection AT jingyang mobilerecommendationbasedonlinkcommunitydetection |