# Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs

## TL;DR

This paper introduces a novel collective mobile sequential recommendation framework for multiple taxicabs, addressing route overlap issues and optimizing travel time through a new mathematical model, evaluation metric, and efficient algorithms.

## Contribution

It formalizes a multi-taxicab recommendation problem, proposes a new evaluation metric, and develops algorithms that outperform conventional methods in simulations.

## Key findings

- Our method significantly reduces route overlap.
- It minimizes total potential travel time.
- Numerical experiments confirm superior performance.

## Abstract

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.