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

摘要

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.