research-article

Multi-User Mobile Sequential Recommendation for Route Optimization

Authors:

Yuefan DengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 5

Article No.: 52, Pages 1 - 28

https://doi.org/10.1145/3360048

Published: 06 July 2020 Publication History

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.

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Index Terms

Multi-User Mobile Sequential Recommendation for Route Optimization
1. Computing methodologies
  1. Parallel computing methodologies
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Mobile information processing systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 5

Special Issue on KDD 2018, Regular Papers and Survey Paper

October 2020

376 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3407672

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2020

Accepted: 01 September 2019

Revised: 01 June 2019

Received: 01 January 2019

Published in TKDD Volume 14, Issue 5

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National Social Science Foundation of China

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Cited By

Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Zong ZTong XZheng MLi Y(2024)Reinforcement Learning for Solving Multiple Vehicle Routing Problem with Time WindowACM Transactions on Intelligent Systems and Technology10.1145/362523215:2(1-19)Online publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1145/3625232
Guo PXiao K(2024)From efficiency to equity: A multi-user paradigm in mobile route optimizationElectronic Commerce Research and Applications10.1016/j.elerap.2024.10145968(101459)Online publication date: Nov-2024
https://doi.org/10.1016/j.elerap.2024.101459
Chen B(2023)Point Cloud Registration via Heuristic Reward Reinforcement LearningStats10.3390/stats60100166:1(268-278)Online publication date: 6-Feb-2023
https://doi.org/10.3390/stats6010016
Li YChe QWang FZhou YLi CWang W(2023)A Multi-Agent Based Method for Large-Scale Route Planning Using Distributed Constraint Optimization2023 IEEE International Conference on Unmanned Systems (ICUS)10.1109/ICUS58632.2023.10318482(1213-1218)Online publication date: 13-Oct-2023
https://doi.org/10.1109/ICUS58632.2023.10318482
Guo PXiao KZhu HMeng Q(2023)Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00021(120-129)Online publication date: 1-Dec-2023
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Yu XZhu SRen Y(2023)Continuous trajectory similarity search with result diversificationFuture Generation Computer Systems10.1016/j.future.2023.02.011143(392-400)Online publication date: Jun-2023
https://doi.org/10.1016/j.future.2023.02.011
Wang CLi KChen L(2023)Deep unified attention-based sequence modeling for online anomalous trajectory detectionFuture Generation Computer Systems10.1016/j.future.2023.02.007144(1-11)Online publication date: Jul-2023
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Zhang HZhao PXian XSheng VHao YCui Z(2023)Click is not equal to purchase: multi-task reinforcement learning for multi-behavior recommendationWorld Wide Web10.1007/s11280-023-01215-626:6(4153-4172)Online publication date: 1-Nov-2023
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