title: Mobile recommendation based on link community detection.
abstract: 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.
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
authors.forename: Kun
authors.identifier: 0000-0002-6377-9205
authors.initials: K
authors.lastname: Deng
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
authors.forename: Jianpei
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D040421:Cell Phone / Q000592:standards*; D063731:Mobile Applications / Q000592:standards*; D012111:Residence Characteristics