Multi-user mobile sequential recommendation: An efficient parallel computing paradigm

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

doi:10.1145/3219819.3220111

Multi-user mobile sequential recommendation: An efficient parallel computing paradigm

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

Management Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

25 Scopus citations

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.

Original language	English (US)
Title of host publication	KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	2624-2633
Number of pages	10
ISBN (Print)	9781450355520
DOIs	https://doi.org/10.1145/3219819.3220111
State	Published - Jul 19 2018
Event	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom Duration: Aug 19 2018 → Aug 23 2018

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/Territory	United Kingdom
City	London
Period	8/19/18 → 8/23/18

Keywords

Mobile sequential recommendation
Parallel computing
Potential travel distance
Simulated annealing

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3219819.3220111

Cite this

Ye, Z., Zhou, W., Zhang, L., Ge, Y., Xiao, K., & Deng, Y. (2018). Multi-user mobile sequential recommendation: An efficient parallel computing paradigm. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2624-2633). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3219819.3220111

Multi-user mobile sequential recommendation: An efficient parallel computing paradigm. / Ye, Zeyang; Zhou, Wenjun; Zhang, Lihao et al.
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 2624-2633 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ye, Z, Zhou, W, Zhang, L, Ge, Y, Xiao, K & Deng, Y 2018, Multi-user mobile sequential recommendation: An efficient parallel computing paradigm. in KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 2624-2633, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 8/19/18. https://doi.org/10.1145/3219819.3220111

Ye Z, Zhou W, Zhang L, Ge Y, Xiao K, Deng Y. Multi-user mobile sequential recommendation: An efficient parallel computing paradigm. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2018. p. 2624-2633. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3219819.3220111

Ye, Zeyang ; Zhou, Wenjun ; Zhang, Lihao et al. / Multi-user mobile sequential recommendation : An efficient parallel computing paradigm. KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. pp. 2624-2633 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{9edc51aa9ba74a708b49ea4c7083190d,

title = "Multi-user mobile sequential recommendation: An efficient parallel computing paradigm",

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.",

keywords = "Mobile sequential recommendation, Parallel computing, Potential travel distance, Simulated annealing",

author = "Zeyang Ye and Wenjun Zhou and Lihao Zhang and Yong Ge and Keli Xiao and Yuefan Deng",

note = "Funding Information: Keli Xiao acknowledges the support of the National Natural Science Foundation of China (NSFC) (Grant # 91746109). Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 ; Conference date: 19-08-2018 Through 23-08-2018",

year = "2018",

month = jul,

day = "19",

doi = "10.1145/3219819.3220111",

language = "English (US)",

isbn = "9781450355520",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

pages = "2624--2633",

booktitle = "KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

}

TY - GEN

T1 - Multi-user mobile sequential recommendation

T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018

AU - Ye, Zeyang

AU - Zhou, Wenjun

AU - Zhang, Lihao

AU - Ge, Yong

AU - Xiao, Keli

AU - Deng, Yuefan

N1 - Funding Information: Keli Xiao acknowledges the support of the National Natural Science Foundation of China (NSFC) (Grant # 91746109). Publisher Copyright: © 2018 Association for Computing Machinery.

PY - 2018/7/19

Y1 - 2018/7/19

N2 - 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.

AB - 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.

KW - Mobile sequential recommendation

KW - Parallel computing

KW - Potential travel distance

KW - Simulated annealing

UR - http://www.scopus.com/inward/record.url?scp=85051473177&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051473177&partnerID=8YFLogxK

U2 - 10.1145/3219819.3220111

DO - 10.1145/3219819.3220111

M3 - Conference contribution

AN - SCOPUS:85051473177

SN - 9781450355520

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 2624

EP - 2633

BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

Y2 - 19 August 2018 through 23 August 2018

ER -

Multi-user mobile sequential recommendation: An efficient parallel computing paradigm

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this