Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs
This addresses route optimization for multiple taxicabs, an incremental improvement over single-taxicab methods.
The paper tackles the problem of recommending routes for multiple taxicabs to avoid excessive overlap, formalizing it as a collective mobile sequential recommendation problem and proposing a new evaluation metric and greedy algorithm. Numerical experiments show their method significantly outperforms conventional methods in trace-driven simulations.
Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multi-taxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes time-varying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In trace-driven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.