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

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Abstract

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.

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Notes

  1. https://foursquare.com/about/.

  2. To check the recall of the ground-truth location using the above candidate generation method, we compute the hit ratios of the ground-truth location among the 5000 nearest locations on the three datasets as follows: 93, 96 and 98%. It can be seen the majority of the ground-truth locations were recalled using 5000 nearest locations.

  3. It was originally proposed for next-basket recommendation in shopping, and we have slightly modified it to adapt to the current two tasks.

  4. https://radimrehurek.com/gensim/index.html.

  5. Due to multi-threading techniques, the time cost does not show a strict linear increase with the increasing of VS.

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Acknowledgements

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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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Wayne Xin Zhao, Ningnan Zhou & Ji-Rong Wen

  2. Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China

    Wayne Xin Zhao & Ji-Rong Wen

  3. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, China

    Wayne Xin Zhao

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Aixin Sun & Jialong Han

  5. HTC Research & Healthcare, San Francisco, CA, USA

    Edward Y. Chang

Authors
  1. Wayne Xin Zhao
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  2. Ningnan Zhou
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  3. Aixin Sun
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  4. Ji-Rong Wen
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  5. Jialong Han
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Correspondence to Jialong Han.

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Zhao, W.X., Zhou, N., Sun, A. et al. A time-aware trajectory embedding model for next-location recommendation. Knowl Inf Syst 56, 559–579 (2018). https://doi.org/10.1007/s10115-017-1107-4

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