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Abstract
Based on the huge volumes of user check-in data in LBSNs, users’ intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information Network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.
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Details
1 School of Computer Science and Engineering, Xi’an University of Technology, 710048 Xi’an, China; Telematics Group, the University of Goettingen, 37077 Goettingen, Germany; Shaanxi Key Laboratory of Network Computing and Security, 710048 Xi’an, China; School of Electronic and Information Engineering, Xi’an Jiaotong University, 710048 Xi’an, China
2 College of Xi’an Innovation, Yan’an University, 710100 Xi’an, China
3 Yan’an University, Yan’an 716000, China
4 National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
5 School of Computer Science and Engineering, Xi’an University of Technology, 710048 Xi’an, China