Knowledge and Information Systems

, Volume 56, Issue 3, pp 559–579 | Cite as

A time-aware trajectory embedding model for next-location recommendation

  • Wayne Xin Zhao
  • Ningnan Zhou
  • Aixin Sun
  • Ji-Rong Wen
  • Jialong Han
  • Edward Y. Chang
Regular Paper


Next-location recommendation is an emerging task with the proliferation of location-based services. It is the task of recommending the next location to visit for a user, given her past check-in records. Although several principled solutions have been proposed for this task, existing studies have not well characterized the temporal factors in the recommendation. From three real-world datasets, our quantitative analysis reveals that temporal factors play an important role in next-location recommendation, including the periodical temporal preference and dynamic personal preference. In this paper, we propose a Time-Aware Trajectory Embedding Model (TA-TEM) to incorporate three kinds of temporal factors in next-location recommendation. Based on distributed representation learning, the proposed TA-TEM jointly models multiple kinds of temporal factors in a unified manner. TA-TEM also enhances the sequential context by using a longer context window. Experiments show that TA-TEM outperforms several competitive baselines.


Next-location recommendation Distributed representation learning Temporal factors 



The authors thank the anonymous reviewers for their valuable and constructive comments. The work was partially supported by National Natural Science Foundation of China under the Grant Number 61502502, Beijing Natural Science Foundation under the Grant Number 4162032, the National Key Basic Research Program (973 Program) of China under Grant No. 2014CB340403, and the open fund with the Grant Number MJUKF201703 from Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University).


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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Wayne Xin Zhao
    • 1
    • 2
    • 3
  • Ningnan Zhou
    • 1
  • Aixin Sun
    • 4
  • Ji-Rong Wen
    • 1
    • 2
  • Jialong Han
    • 4
  • Edward Y. Chang
    • 5
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Beijing Key Laboratory of Big Data Management and Analysis MethodsBeijingChina
  3. 3.Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)FuzhouChina
  4. 4.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  5. 5.HTC Research & HealthcareSan FranciscoUSA

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