Multi-User Mobile Sequential Recommendation for Route Optimization

Keli Xiao, Zeyang Ye, Lihao Zhang, Wenjun Zhou, Yong Ge, Yuefan Deng

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We enhance the mobile sequential recommendation (MSR) model and address some critical issues in existing formulations by proposing three new forms of the MSR from a multi-user perspective. The multi-user MSR (MMSR) model searches optimal routes for multiple drivers at different locations while disallowing overlapping routes to be recommended. To enrich the properties of pick-up points in the problem formulation, we additionally consider the pick-up capacity as an important feature, leading to the following two modified forms of the MMSR: MMSR-m and MMSR-d. The MMSR-m sets a maximum pick-up capacity for all urban areas, while the MMSR-d allows the pick-up capacity to vary at different locations. We develop a parallel framework based on the simulated annealing to numerically solve the MMSR problem series. Also, a push-point method is introduced to improve our algorithms further for the MMSR-m and the MMSR-d, which can handle the route optimization in more practical ways. Our results on both real-world and synthetic data confirmed the superiority of our problem formulation and solutions under more demanding practical scenarios over several published benchmarks.

Original languageEnglish (US)
Article number3360048
JournalACM Transactions on Knowledge Discovery from Data
Volume14
Issue number5
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Mobile sequential recommendation
  • parallel computing
  • potential traveling distance
  • simulated annealing
  • trajectory data analysis

ASJC Scopus subject areas

  • Computer Science(all)

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