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The Scientific World Journal
Volume 2014 (2014), Article ID 259156, 13 pages
Research Article

Mobile Recommendation Based on Link Community Detection

College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

Received 4 June 2014; Revised 20 July 2014; Accepted 22 July 2014; Published 26 August 2014

Academic Editor: Wanneng Shu

Copyright © 2014 Kun Deng et al. 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.


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.