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Frontiers of Computer Science

, Volume 14, Issue 2, pp 314–333 | Cite as

NEXT: a neural network framework for next POI recommendation

  • Zhiqian Zhang
  • Chenliang LiEmail author
  • Zhiyong Wu
  • Aixin Sun
  • Dengpan Ye
  • Xiangyang Luo
Research Article

Abstract

The task of next POI recommendations has been studied extensively in recent years. However, developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to smoothly handle cold-start cases are also a difficult topic. Inspired by the recent success of neural networks in many areas, in this paper, we propose a simple yet effective neural network framework, named NEXT, for next POI recommendations. NEXT is a unified framework to learn the hidden intent regarding user’s next move, by incorporating different factors in a unified manner. Specifically, in NEXT, we incorporate meta-data information, e.g., user friendship and textual descriptions of POIs, and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based solutions. Experimental results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendations. Further experiments show inherent ability of NEXT in handling cold-start.

Keywords

POI neural networks POI recommendation 

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Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant Nos. 61872278, 61502344, 1636219, U1636101), Natural Science Foundation of Hubei Province (2017CFB502), Academic Team Building Plan for Young Scholars from Wuhan University (Whu2016012) and Singapore Ministry of Education Academic Research Fund Tier 2 (MOE2014-T2-2-066). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

Supplementary material

11704_2018_8011_MOESM1_ESM.pdf (246 kb)
NEXT: a neural network framework for next POI recommendation

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhiqian Zhang
    • 1
  • Chenliang Li
    • 1
    Email author
  • Zhiyong Wu
    • 2
  • Aixin Sun
    • 3
  • Dengpan Ye
    • 1
  • Xiangyang Luo
    • 4
  1. 1.Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and EngineeringWuhan UniversityWuhanChina
  2. 2.Department of Computer ScienceThe University of Hong KongHong KongChina
  3. 3.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  4. 4.State Key Lab of Mathematical Engineering and Advanced ComputingZhengzhouChina

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