TY - JOUR
T1 - Multi-User Mobile Sequential Recommendation for Route Optimization
AU - Xiao, Keli
AU - Ye, Zeyang
AU - Zhang, Lihao
AU - Zhou, Wenjun
AU - Ge, Yong
AU - Deng, Yuefan
N1 - Funding Information:
K. Xiao and Z. Ye contributed equally to this article. This work was partially supported by the National Social Science Foundation of China (# 18BFX096) and the National Natural Science Foundation of China (# 91746109). Authors’ addresses: K. Xiao (corresponding author), College of Business, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794; email: [email protected]; Z. Ye, L. Zhang, and Y. Deng, Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794; emails: [email protected], {lihao.zhang, yuefan.deng}@stonybrook.edu; W. Zhou, Department of Business Analytics and Statistics, Haslam College of Business, University of Tennessee Knoxville, 916 Volunteer Blvd., Knoxville, TN 37996-0532; email: [email protected]; Y. Ge, Department of Management Information Systems, Eller College of Management, University of Arizona, 1130 E Helen St, Tucson, AZ 85721; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2020 Association for Computing Machinery. 1556-4681/2020/07-ART52 $15.00 https://doi.org/10.1145/3360048
Publisher Copyright:
© 2020 ACM.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - Mobile sequential recommendation
KW - parallel computing
KW - potential traveling distance
KW - simulated annealing
KW - trajectory data analysis
UR - http://www.scopus.com/inward/record.url?scp=85092312153&partnerID=8YFLogxK
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U2 - 10.1145/3360048
DO - 10.1145/3360048
M3 - Article
AN - SCOPUS:85092312153
SN - 1556-4681
VL - 14
JO - ACM Transactions on Knowledge Discovery from Data
JF - ACM Transactions on Knowledge Discovery from Data
IS - 5
M1 - 3360048
ER -