Multi-User Mobile Sequential Recommendation: An Efficient Parallel Computing Paradigm

Authors:
Zeyang Ye Stony Brook University
Lihao Zhang Stony Brook University
Keli Xiao Stony Brook University
Wenjun Zhou University of Tennessee Knoxville
Yong Ge University of Arizona
Yuefan Deng Stony Brook University

Introduction:

This paper studies The classic mobile sequential recommendation (MSR) problem. The authors formalize a new multi-user MSR (MMSR) problem that locates optimal routes for a group of drivers with different starting positions.

Abstract:

The classic mobile sequential recommendation (MSR) problem aims to provide the optimal route to taxi drivers for minimizing the potential travel distance before they meet next passengers. However, the problem is designed from the view of a single user and may lead to overlapped recommendations and cause traffic problems. Existing approaches usually contain an offline pruning process with extremely high computational cost, given a large number of pick-up points. To this end, we formalize a new multi-user MSR (MMSR) problem that locates optimal routes for a group of drivers with different starting positions. We develop two efficient methods, PSAD and PSAD-M, for solving the MMSR problem by ganging parallel computing and simulated annealing. Our methods outperform several existing approaches, especially for high-dimensional MMSR problems, with a record-breaking performance of 180x speedup using 384 cores.

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