  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.
