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Microblog User Recommendation Based on Particle Swarm Optimization

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Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization (PSO) for Microblog network.Specifically,a PSO-based algorithm is developed to learn the user influence,where not only the number of followers is incorporated,but also the interactions among users (e.g.,forwarding and commenting on other users' tweets).Three social factors,the influence and the activity of the target user,together with the coherence between users,are fused to improve the performance of proposed recommendation strategy.Experimental results show that,compared to the well-known PageRank-based algorithm,the proposed strategy performs much better in terms of precision and recall and it can effectively avoid a biased result caused by celebrity effect and zombie fans effect.

particle swarm optimizationMicroblog social networkuser recommendationuser influence

Ling Xing、Qiang Ma、Ling Jiang

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School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

School of Mathematics Science and Computer, Wuyi University, Wuyishan 354300, China

This research was supported by National Natural Science Foundation of ChinaApplied Basic Research Programs of Sichuan Science and Technology DepartmentBasic Research Plan in SWUST

611711092014JY021513zx9101

2017

中国通信(英文版)

中国通信(英文版)

CSTPCDCSCDSCI
影响因子：0.463
ISSN：1673-5447
年,卷(期)：2017.14(5)
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