research-article

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

Authors:

Yuefan DengAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2624 - 2633

https://doi.org/10.1145/3219819.3220111

Published: 19 July 2018 Publication History

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.

Supplementary Material

MP4 File (ye_mobile_sequential_recommendation.mp4)

Download
291.71 MB

References

[1]

David L. Applegate, Robert E. Bixby, Vasek Chvatal, and William J. Cook. 2011. The traveling salesman problem: a computational study. Princeton university press.

Digital Library

[2]

RaúL BañOs, Julio Ortega, Consolación Gil, Antonio FernáNdez, and Francisco De Toro. 2013. A simulated annealing-based parallel multi-objective approach to vehicle routing problems with time windows. Expert Systems with Applications 40, 5 (2013), 1696--1707.

Digital Library

[3]

King-Wai Chu, Yuefan Deng, and John Reinitz. 1999. Parallel simulated annealing by mixing of states. J. Comput. Phys. 148, 2 (1999), 646--662.

Digital Library

[4]

George B Dantzig and John H Ramser. 1959. The truck dispatching problem. Management Science 6, 1 (1959), 80--91.

Digital Library

[5]

Frederica Darema, Scott Kirkpatrick, and V. Alan Norton. 1987. Parallel algorithms for chip placement by simulated annealing. IBM Journal of Research and Development 31, 3 (1987), 391--402.

Digital Library

[6]

Frank J Fabozzi and Keli Xiao. 2017. Explosive rents: The real estate market dynamics in exuberance. The Quarterly Review of Economics and Finance 66 (2017), 100--107.

[7]

Frank J Fabozzi and Keli Xiao. 2018. The Timeline Estimation of Bubbles: The Case of Real Estate. Real Estate Economics (2018).

[8]

Yong Ge, Chuanren Liu, Hui Xiong, and Jian Chen. 2011. A taxi business intelligence system. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 735--738.

Digital Library

[9]

Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, and Michael Pazzani. 2010. An energy--efficient mobile recommender system. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 899--908.

Digital Library

[10]

Jianbin Huang, Xuejun Huangfu, Heli Sun, Hui Li, Peixiang Zhao, Hong Cheng, and Qinbao Song. 2015. Backward path growth for efficient mobile sequential recommendation. IEEE Transactions on Knowledge and Data Engineering 27, 1 (2015), 46--60.

[11]

Scott Kirkpatrick, C Daniel Gelatt, and Mario P Vecchi. 1983. Optimization by simulated annealing. Science 220 (1983), 671--680.

[12]

Chuanren Liu, Yong Ge, Hui Xiong, Keli Xiao, Wei Geng, and Matt Perkins. 2014. Proactive workflow modeling by stochastic processes with application to healthcare operation and management. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1593-- 1602.

Digital Library

[13]

Chuanren Liu, Hui Xiong, Spiros Papadimitriou, Yong Ge, and Keli Xiao. 2017. A proactive workflow model for healthcare operation and management. IEEE transactions on knowledge and data engineering 29, 3 (2017), 586--598.

Digital Library

[14]

Zhihao Lou and John Reinitz. 2016. Parallel simulated annealing using an adaptive resampling interval. Parallel computing 53 (2016), 23--31.

Digital Library

[15]

Shuo Ma, Yu Zheng, and Ouri Wolfson. 2013. T-share: A large-scale dynamic taxi ridesharing service. In The 2013 IEEE 29th International Conference on Data Engineering (ICDE). 410--421.

Digital Library

[16]

Shuo Ma, Yu Zheng, and OuriWolfson. 2015. Real-time city-scale taxi ridesharing. IEEE Transactions on Knowledge and Data Engineering 27, 7 (2015), 1782--1795.

Digital Library

[17]

SamirWMahfoud and David E Goldberg. 1995. Parallel recombinative simulated annealing: a genetic algorithm. Parallel computing 21, 1 (1995), 1--28.

Digital Library

[18]

Jason W Powell, Yan Huang, Favyen Bastani, and Minhe Ji. 2011. Towards reducing taxicab cruising time using spatio-temporal profitability maps. In International Symposium on Spatial and Temporal Databases. Springer, 242--260.

Digital Library

[19]

Meng Qu, Hengshu Zhu, Junming Liu, Guannan Liu, and Hui Xiong. 2014. A cost-effective recommender system for taxi drivers. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 45--54.

Digital Library

[20]

Andrew Sohn. 1996. Generalized speculative computation of parallel simulated annealing. Annals of Operations Research 63, 1 (1996), 29--55.

[21]

Paolo Toth and Daniele Vigo. 2002. The vehicle routing problem. SIAM.

[22]

RanWang, Chi-Yin Chow, Yan Lyu, Victor CS Lee, Sam Kwong, Yanhua Li, and Jia Zeng. 2018. Taxirec: recommending road clusters to taxi drivers using rankingbased extreme learning machines. IEEE Transactions on Knowledge and Data Engineering 30, 3 (2018), 585--598.

[23]

Keli Xiao, Qi Liu, Chuanren Liu, and Hui Xiong. 2018. Price Shock Detection With an Influence-Based Model of Social Attention. ACM Transactions on Management Information Systems 9, 1 (2018), 2.

Digital Library

[24]

Zeyang Ye, Keli Xiao, and Yuefan Deng. 2015. Investigation of Simulated Annealing Cooling Schedule for Mobile Recommendations. In Proceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 1078--1084.

Digital Library

[25]

Zeyang Ye, Keli Xiao, Yong Ge, and Yuefan Deng. 2018. Applying Simulated Annealing and Parallel Computing to the Mobile Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering (2018).

[26]

Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie, and Guangzhong Sun. 2011. Where to find my next passenger. In Proceedings of the 13th international conference on Ubiquitous computing. 109--118.

Digital Library

[27]

Nicholas Jing Yuan, Yu Zheng, Liuhang Zhang, and Xing Xie. 2013. T-finder: A recommender system for finding passengers and vacant taxis. IEEE Transactions on Knowledge and Data Engineering 25, 10 (2013), 2390--2403.

Digital Library

[28]

Li Zhang, Keli Xiao, Qi Liu, Yefan Tao, and Yuefan Deng. 2015. Modeling social attention for stock analysis: An influence propagation perspective. In Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 609--618.

Digital Library

[29]

Hengshu Zhu, Enhong Chen, Kuifei Yu, Huanhuan Cao, Hui Xiong, and Jilei Tian. 2012. Mining personal context-aware preferences for mobile users. In Proceedings of the 2012 IEEE International Conference on Data Mining (ICDM). IEEE, 1212--1217.

Digital Library

Cited By

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
Zhang JYu JShang SChen LFeng S(2023)Continuous frequent contact detection over moving objectsGeoInformatica10.1007/s10707-023-00501-928:2(271-290)Online publication date: 17-Jul-2023
https://doi.org/10.1007/s10707-023-00501-9
Guo PYe ZXiao KZhu W(2022)Weighted Aggregating Stochastic Gradient Descent for Parallel Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304789434:10(5037-5050)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TKDE.2020.3047894
Show More Cited By

Index Terms

Multi-User Mobile Sequential Recommendation: An Efficient Parallel Computing Paradigm
1. Computing methodologies
  1. Parallel computing methodologies
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Mobile information processing systems

Recommendations

Multi-User Mobile Sequential Recommendation for Route Optimization
Special Issue on KDD 2018, Regular Papers and Survey Paper

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 ...
User Popularity Preference Aware Sequential Recommendation
Computational Science – ICCS 2023
Abstract
In recommender systems, users’ preferences for item popularity are diverse and dynamic, which reveals the different items that users prefer. Therefore, identifying user popularity preferences are significant for personalized recommendations. ...
Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining

Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China

Conference

KDD '18

Sponsor:

KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

Acceptance Rates

KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
923
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)1

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

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
Zhang JYu JShang SChen LFeng S(2023)Continuous frequent contact detection over moving objectsGeoInformatica10.1007/s10707-023-00501-928:2(271-290)Online publication date: 17-Jul-2023
https://doi.org/10.1007/s10707-023-00501-9
Guo PYe ZXiao KZhu W(2022)Weighted Aggregating Stochastic Gradient Descent for Parallel Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304789434:10(5037-5050)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TKDE.2020.3047894
Guo PXiao KYe ZZhu HZhu W(2022)Intelligent career planning via stochastic subsampling reinforcement learningScientific Reports10.1038/s41598-022-11872-812:1Online publication date: 18-May-2022
https://doi.org/10.1038/s41598-022-11872-8
Guo PXiao KYe ZZhu W(2021)Route Optimization via Environment-Aware Deep Network and Reinforcement LearningACM Transactions on Intelligent Systems and Technology10.1145/346164512:6(1-21)Online publication date: 16-Dec-2021
https://dl.acm.org/doi/10.1145/3461645
Zhang YDuan QShao CShi Y(2021)Parallel Population-Based Simulated Annealing for High-Dimensional Black-Box Optimization2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659957(01-07)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9659957
Huang CZhao JYin D(2021)Purchase Intent Forecasting with Convolutional Hierarchical Transformer Networks2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00281(2488-2498)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00281
Zhang YDu BZhang LWu JGupta RLiu YShah MRajan STang JPrakash B(2020)Parallel DNN Inference Framework Leveraging a Compact RISC-V ISA-based Multi-core SystemProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403105(627-635)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403105
Xiao KYe ZZhang LZhou WGe YDeng Y(2020)Multi-User Mobile Sequential Recommendation for Route OptimizationACM Transactions on Knowledge Discovery from Data10.1145/336004814:5(1-28)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1145/3360048
Meng QZhu HXiao KZhang LXiong HTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)A Hierarchical Career-Path-Aware Neural Network for Job Mobility PredictionProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330969(14-24)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330969
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten