Fine-Grained User Location Prediction using Meta-Path Context with Attention Mechanism

Authors

  • Zhixiao Wang Xi’an University of Technology, China, Xi’an Jiaotong University, China and Shaanxi Key-Lab of Network Computing and Security, China
  • Wenyao Yan Xi’an Innovation of Yan’an University, China
  • Ang Gao National Satellite Meteorological Center, China Meteorological Administration, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2032

Keywords:

Geo-Social Network (GSN), Attention Mechanism, Meta-path Contexts Learning, Location-based Social Networks (LBSNs), Pairwise Learning, User Location Prediction

Abstract

The prevalence of Location-Based Social Networks (LBSNs) significantly improves the location-aware capability of services by providing Geo-tagged information. Relied on a great number of user check-in data in the location-based social networks, their essential mobility modes are able to be comprehensively studied, which is basic for forecasting the next venue where a specific user is going to visit considering his relevant historical check-in data. Since there exist different kinds of nodes and interactions between nodes, these information could be look upon as a network that is made up of heterogeneous information. In this network a few of different semantic meta paths could be obtained. Enlightened from the competitive advantage of embedding method relied upon meta-path contexts in the heterogeneous information network, we study a joint deep learning scheme exploring different meta-path context information to forecast fine-grained location. In order to capture different semantics in a user-location interaction, we adopt a simple but high-efficient attention method to learn the meta-path importance or weights. In the terms of model optimization, considering we have only positive sample data and there exists intrinsically latent feedback in check-in information, herein a pairwise learning method is utilized for maximizing the margin between visited and invisible venues. Experiment in different data-sets validate the competitive performance of the suggested approach under different assessment criterion.

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Author Biographies

Zhixiao Wang, Xi’an University of Technology, China, Xi’an Jiaotong University, China and Shaanxi Key-Lab of Network Computing and Security, China

Zhixiao Wang, associate professor, was born in Shaanxi China in 1976. He received the B.S., M.S., and Ph.D. degrees in Computer Science respectively from Hebei GEO University in 2000, Xi’an University of Technology in 2004, and Xi’an Jiaotong University in 2014. He is a Member of IEEE and a Member of CCF. Since 2017, he has been an Associate Professor with the School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China. Prior to that, he studied at Institute of Mathematics and Computer Science, University of Göttingen in Germany from 2010 to 2011; he was a postdoctoral research fellow at Xi’an Jiaotong University; he was also a postdoctoral research fellow and a visiting scholar at University of Göttingen during from 2017 to 2019. He is Editor in Chief of two books and the author of two books, about 50 publications. His research interests include IoT, big data, machine learning, etc.

Wenyao Yan, Xi’an Innovation of Yan’an University, China

Wenyao Yan, Associate professor, was born in JiLin China in 1979. She was received her Master degree in computer science from Northeast Electric Power University in 2006. She focuses on big data, intelligent information processing, etc.

Ang Gao, National Satellite Meteorological Center, China Meteorological Administration, China

Ang Gao was born in Xi’an city of Shaanxi, China in 1978. He received his Ph.D. degree in Computer Science from Xi’an Jiaotong University. His research interests include machine learning, network security, etc.

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Published

2021-06-10

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Articles