PathRec: visual analysis of travel route recommendations

Dawei Chen; Dongwoo Kim; Lexing Xie; Minjeong Shin; Aditya Krishna Menon; Cheng Soon Ong; Iman Avazpour; John Grundy

doi:10.1145/3109859.3109983

PathRec: visual analysis of travel route recommendations

Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

9 Citations (Scopus)

Abstract

We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.

Original language	English
Title of host publication	Proceedings of the Eleventh ACM Conference on Recommender Systems
Editors	Shlomo Berkovsky, Alexander Tuzhilin
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	364-365
Number of pages	2
ISBN (Electronic)	9781450346528
DOIs	https://doi.org/10.1145/3109859.3109983
Publication status	Published - 2017
Externally published	Yes
Event	ACM International Conference on Recommender Systems 2017 - Como, Italy Duration: 27 Aug 2017 → 31 Aug 2017 Conference number: 11th https://recsys.acm.org/recsys17/

Conference

Conference	ACM International Conference on Recommender Systems 2017
Abbreviated title	RecSys 2017
Country/Territory	Italy
City	Como
Period	27/08/17 → 31/08/17
Internet address	https://recsys.acm.org/recsys17/

Keywords

Learning to rank
Route visualization
Travel recommendation

Access to Document

10.1145/3109859.3109983

Cite this

@inproceedings{52af03d04f2243c1ad47b151edf5013a,

title = "PathRec: visual analysis of travel route recommendations",

abstract = "We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.",

keywords = "Learning to rank, Route visualization, Travel recommendation",

author = "Dawei Chen and Dongwoo Kim and Lexing Xie and Minjeong Shin and Menon, {Aditya Krishna} and Ong, {Cheng Soon} and Iman Avazpour and John Grundy",

year = "2017",

doi = "10.1145/3109859.3109983",

language = "English",

pages = "364--365",

editor = "Berkovsky, {Shlomo } and Alexander Tuzhilin",

booktitle = "Proceedings of the Eleventh ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "ACM International Conference on Recommender Systems 2017, RecSys 2017 ; Conference date: 27-08-2017 Through 31-08-2017",

url = "https://recsys.acm.org/recsys17/",

}

Chen, D, Kim, D, Xie, L, Shin, M, Menon, AK, Ong, CS, Avazpour, I & Grundy, J 2017, PathRec: visual analysis of travel route recommendations. in S Berkovsky & A Tuzhilin (eds), Proceedings of the Eleventh ACM Conference on Recommender Systems. Association for Computing Machinery (ACM), New York NY USA, pp. 364-365, ACM International Conference on Recommender Systems 2017, Como, Italy, 27/08/17. https://doi.org/10.1145/3109859.3109983

PathRec: visual analysis of travel route recommendations. / Chen, Dawei; Kim, Dongwoo; Xie, Lexing et al.
Proceedings of the Eleventh ACM Conference on Recommender Systems. ed. / Shlomo Berkovsky; Alexander Tuzhilin. New York NY USA: Association for Computing Machinery (ACM), 2017. p. 364-365.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

TY - GEN

T1 - PathRec

T2 - ACM International Conference on Recommender Systems 2017

AU - Chen, Dawei

AU - Kim, Dongwoo

AU - Xie, Lexing

AU - Shin, Minjeong

AU - Menon, Aditya Krishna

AU - Ong, Cheng Soon

AU - Avazpour, Iman

AU - Grundy, John

N1 - Conference code: 11th

PY - 2017

Y1 - 2017

N2 - We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.

AB - We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.

KW - Learning to rank

KW - Route visualization

KW - Travel recommendation

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U2 - 10.1145/3109859.3109983

DO - 10.1145/3109859.3109983

M3 - Conference Paper

AN - SCOPUS:85030458445

SP - 364

EP - 365

BT - Proceedings of the Eleventh ACM Conference on Recommender Systems

A2 - Berkovsky, Shlomo

A2 - Tuzhilin, Alexander

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

Y2 - 27 August 2017 through 31 August 2017

ER -

PathRec: visual analysis of travel route recommendations

Abstract

Conference

Keywords

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