A point-of-interest suggestion algorithm in Multi-source geo-social networks

被引:22
|
作者
Xiong, Xi [1 ,4 ]
Qiao, Shaojie [2 ]
Li, Yuanyuan [3 ]
Han, Nan [5 ,6 ]
Yuan, Guan [7 ]
Zhang, Yongqing [8 ]
机构
[1] Chengdu Univ Informat Technol, Sch Cybersecur, Chengdu 610225, Peoples R China
[2] Chengdu Univ Informat Technol, Sch Software Engn, Chengdu 610225, Peoples R China
[3] Sichuan Univ, Mental Hlth Ctr, West China Hosp, Chengdu 610041, Peoples R China
[4] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610065, Peoples R China
[5] Chengdu Univ Informat Technol, Sch Management, Chengdu 610103, Peoples R China
[6] Guangdong Prov Engn Ctr China Made High Performan, Guangdong Prov Key Lab Popular High Performance C, Guangzhou 518060, Peoples R China
[7] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
[8] Chengdu Univ Informat Technol, Sch Comp Sci, Chengdu 610225, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Location-based social networks; POI suggestion; Geo-social networks; Probabilistic graphical model; Gibbs sampling; MODEL;
D O I
10.1016/j.engappai.2019.103374
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Newly emerging location-based social network (LBSN) services provide us with new platforms to share interests and individual experience based on their activity history. The problems of data sparsity and user distrust in LBSNs create a severe challenge for traditional recommender systems. Moreover, users' behaviors in LBSNs show an obvious spatio-temporal pattern. Valuable extra information from microblog-based social networks (MBSNs) can be utilized to improve the effectiveness of POI suggestion. In this study, we propose a latent probabilistic generative model called MTAS, which can accurately capture the underlying information in users' words extracted from both LBSNs and MBSNs by taking into consideration the decision probability, a latent variable indicating a user's tendency to publish a review in LBSNs or MBSNs. Then, the parameters of the MTAS model can be inferred by the Gibbs sampling method in an effective manner. Based on MTAS, we design an effective framework to fulfill the top-k suggestion. Extensive experiments on two real geo-social networks show that MTAS achieves better performance than existing state-of-the-art methods.
引用
收藏
页数:11
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