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TAILOR: time-aware facility location recommendation based on massive trajectories
Knowledge and Information Systems ( IF 2.7 ) Pub Date : 2020-05-20 , DOI: 10.1007/s10115-020-01477-w
Zhixin Qi , Hongzhi Wang , Tao He , Chunnan Wang , Jianzhong Li , Hong Gao

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.

中文翻译:

裁缝:基于大量轨迹的时间感知设施位置推荐

在传统设施位置建议中,目标是选择最佳位置,以最大程度地覆盖用户或为用户提供便利。但是,由于用户的行为习惯通常受时间影响,因此在建议中不应忽略时间影响。在本文中,我们考虑时间因素,研究了时间感知设施位置推荐问题。为解决此问题,我们开发了一个框架TAILOR,该框架结合了时间影响,用户覆盖范围和用户便利性。基于TAILOR,我们导出了一个(1- \(\ frac {1} {e} \))逼近的贪婪算法和一个(\(\ frac {1} {4} \)的在线算法)竞争率。广泛的实验评估和两个案例研究证明了所提出方法的有效性和有效性。
更新日期:2020-05-20
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