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TAILOR: time-aware facility location recommendation based on massive trajectories

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Abstract

In traditional facility location recommendations, the objective is to select the best locations which maximize the coverage or convenience of users. However, since users’ behavioral habits are often influenced by time, the temporal impacts should not be neglected in recommendation. In this paper, we study the problem of time-aware facility location recommendation problem, taking the time factor into account. To solve this problem, we develop a framework, TAILOR, which incorporates the temporal influence, user-coverage, and user-convenience. Based on TAILOR, we derive a greedy algorithm with (1-\(\frac{1}{e}\))-approximation and an online algorithm with (\(\frac{1}{4}\))-competitive ratio. Extensive experimental evaluation and two case studies demonstrate the efficiency and effectiveness of the proposed approaches.

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Acknowledgements

This paper was partially supported by NSFC grant U1866602, 61602129, 61772157, CCF-Huawei Database System Innovation Research Plan DBIR2019005B and Microsoft Research Asia.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Zhixin Qi, Hongzhi Wang, Tao He, Chunnan Wang, Jianzhong Li & Hong Gao

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  1. Zhixin Qi
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  2. Hongzhi Wang
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  3. Tao He
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  4. Chunnan Wang
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  5. Jianzhong Li
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  6. Hong Gao
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Correspondence to Hongzhi Wang.

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Qi, Z., Wang, H., He, T. et al. TAILOR: time-aware facility location recommendation based on massive trajectories. Knowl Inf Syst 62, 3783–3810 (2020). https://doi.org/10.1007/s10115-020-01477-w

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