research-article

Learning Points and Routes to Recommend Trajectories

Authors:

Cheng Soon Ong,

Lexing XieAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2227 - 2232

https://doi.org/10.1145/2983323.2983672

Published: 24 October 2016 Publication History

Abstract

The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transition patterns between POIs that enable us to recommend probable routes. In addition, a probabilistic model is proposed to combine the results of POI ranking and the POI to POI transitions. We propose a new F₁ score on pairs of POIs that capture the order of visits. Empirical results show that our approach improves on recent methods, and demonstrate that combining points and routes enables better trajectory recommendations.

References

[1]

J. Bao, Y. Zheng, D. Wilkie, and M. Mokbel. Recommendations in location-based social networks: a survey. GeoInformatica, 19(3):525--565, 2015.

Digital Library

[2]

R. Baraglia, C. I. Muntean, F. M. Nardini, and F. Silvestri. LearNext: learning to predict tourists movements. CIKM '13, pages 751--756. ACM, 2013.

Digital Library

[3]

C. Chen, D. Zhang, B. Guo, X. Ma, G. Pan, and Z. Wu. TripPlanner: Personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Transactions on Intelligent Transportation Systems, 16(3):1259--1273, 2015.

Digital Library

[4]

C. Cheng, H. Yang, M. R. Lyu, and I. King. Where you like to go next: Successive point-of-interest recommendation. IJCAI '13, pages 2605--2611. AAAI Press, 2013.

Digital Library

[5]

M. De Choudhury, M. Feldman, S. Amer-Yahia, N. Golbandi, R. Lempel, and C. Yu. Automatic construction of travel itineraries using social breadcrumbs. HT '10, pages 35--44. ACM, 2010.

Digital Library

[6]

Flickr. Flickr photos on the map.surlhttps://www.flickr.com/map, retrieved May 2016.

[7]

Foursquare. FourSquare: about us.surlhttps://foursquare.com/about, retrieved May 2016.

[8]

H. Gao, J. Tang, X. Hu, and H. Liu. Exploring temporal effects for location recommendation on location-based social networks. RecSys '13, pages 93--100. ACM, 2013.

Digital Library

[9]

A. Gionis, T. Lappas, K. Pelechrinis, and E. Terzi. Customized tour recommendations in urban areas. WSDM '14, pages 313--322. ACM, 2014.

Digital Library

[10]

Gurobi. Gurobi Optimization.surlhttp://www.gurobi.com, retrieved May 2016.

[11]

H.-P. Hsieh and C.-T. Li. Mining and planning time-aware routes from check-in data. CIKM '14, pages 481--490. ACM, 2014.

Digital Library

[12]

C.-P. Lee and C.-b. Lin. Large-scale linear rankSVM. Neural computation, 26(4):781--817, 2014.

Digital Library

[13]

D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. KDD '14, pages 831--840. ACM, 2014.

Digital Library

[14]

K. H. Lim, J. Chan, C. Leckie, and S. Karunasekera. Personalized tour recommendation based on user interests and points of interest visit durations. IJCAI '15, 2015.

Digital Library

[15]

Q. Liu, S. Wu, L. Wang, and T. Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. AAAI '16, 2016.

[16]

Y. Liu, W. Wei, A. Sun, and C. Miao. Exploiting geographical neighborhood characteristics for location recommendation. CIKM '14, pages 739--748. ACM, 2014.

Digital Library

[17]

E. H.-C. Lu, C.-Y. Chen, and V. S. Tseng. Personalized trip recommendation with multiple constraints by mining user check-in behaviors. SIGSPATIAL '12, pages 209--218. ACM, 2012.

Digital Library

[18]

X. Lu, C. Wang, J.-M. Yang, Y. Pang, and L. Zhang. Photo2Trip: Generating travel routes from geo-tagged photos for trip planning. MM '10, pages 143--152. ACM, 2010.

Digital Library

[19]

C. H. Papadimitriou and K. Steiglitz. Combinatorial optimization: algorithms and complexity. Dover Publications, 1998.

Digital Library

[20]

S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized Markov chains for next-basket recommendation. WWW '10, pages 811--820. ACM, 2010.

Digital Library

[21]

Y. Shi, P. Serdyukov, A. Hanjalic, and M. Larson. Personalized landmark recommendation based on geotags from photo sharing sites. ICWSM '11, 2011.

[22]

R. W. Sinnott. Virtues of the haversine. Sky and telescope, 68(2):159, 1984.

[23]

B. Thomee, B. Elizalde, D. A. Shamma, K. Ni, G. Friedland, D. Poland, D. Borth, and L.-J. Li. YFCC100M: The new data in multimedia research. Communications of the ACM, 59(2):64--73, 2016.

Digital Library

[24]

Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of-interest recommendation. SIGIR '13, pages 363--372. ACM, 2013.

Digital Library

[25]

Q. Yuan, G. Cong, and A. Sun. Graph-based point-of-interest recommendation with geographical and temporal influences. CIKM '14, pages 659--668. ACM, 2014.

Digital Library

[26]

W. Zhang and J. Wang. Location and time aware social collaborative retrieval for new successive point-of-interest recommendation. CIKM '15, pages 1221--1230. ACM, 2015.

Digital Library

[27]

Y. Zheng. Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology, 6(3):29, 2015.

Digital Library

[28]

Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3):38, 2014.

Digital Library

[29]

Y.-T. Zheng, Z.-J. Zha, and T.-S. Chua. Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology, 3(3):56:1--56:18, May 2012.

Digital Library

Cited By

Shu WXu KTai WZhong TWang YZhou FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Analyzing and Mitigating Repetitions in Trip RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657970(2276-2280)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657970
Zhang SLuo ZYang LTeng FLi T(2024)A survey of route recommendations: Methods, applications, and opportunitiesInformation Fusion10.1016/j.inffus.2024.102413(102413)Online publication date: Apr-2024
https://doi.org/10.1016/j.inffus.2024.102413
Chen JJiang WWu JLi KLi K(2023)Dynamic Personalized POI Sequence Recommendation with Fine-Grained ContextsACM Transactions on Internet Technology10.1145/358368723:2(1-28)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3583687
Show More Cited By

Index Terms

Learning Points and Routes to Recommend Trajectories
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social networking sites
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Extracting visited points of interest from vehicle trajectories
GeoRich '17: Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data

Identifying visited points of interest (PoIs) from vehicle trajectories remains an open problem that is difficult due to vehicles parking often at some distance from the visited PoI and due to some regions having a high PoI density. We propose a visited ...
Learning Location Correlation from GPS Trajectories
MDM '10: Proceedings of the 2010 Eleventh International Conference on Mobile Data Management

People’s location histories imply the location correlation that states the relations between geographical locations in the space of human behavior. With the correlation, we can enable many valuable services, such as location recommendation and sales ...
Exploiting user behavior learning for personalized trajectory recommendations
Abstract
With increasing popularity of mobile devices and flourish of social networks, a large number of trajectory data is accumulated. Trajectory data contains a wealth of information, including spatiality, time series, and other external descriptive ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 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: 24 October 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Australian Research Council

Conference

CIKM'16

Sponsor:

CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

82
Total Citations
View Citations
642
Total Downloads

Downloads (Last 12 months)50
Downloads (Last 6 weeks)5

Reflects downloads up to 01 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shu WXu KTai WZhong TWang YZhou FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Analyzing and Mitigating Repetitions in Trip RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657970(2276-2280)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657970
Zhang SLuo ZYang LTeng FLi T(2024)A survey of route recommendations: Methods, applications, and opportunitiesInformation Fusion10.1016/j.inffus.2024.102413(102413)Online publication date: Apr-2024
https://doi.org/10.1016/j.inffus.2024.102413
Chen JJiang WWu JLi KLi K(2023)Dynamic Personalized POI Sequence Recommendation with Fine-Grained ContextsACM Transactions on Internet Technology10.1145/358368723:2(1-28)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3583687
Xu JTao WChen ZHuang JLiu HWen HNi SDai QGu YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)PlanRanker: Towards Personalized Ranking of Train Transfer PlansProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599887(5315-5326)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599887
Gao QZhou FZhang KZhang FTrajcevski G(2023)Adversarial Human Trajectory Learning for Trip RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.305810234:4(1764-1776)Online publication date: Apr-2023
https://doi.org/10.1109/TNNLS.2021.3058102
Ding LZhao DWang ZWang GTan CFan LMa H(2023)Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2022.318834422:10(5949-5962)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TMC.2022.3188344
Rashid SAli MCheema M(2023)DeepAltTrip: Top-K Alternative Itineraries for Trip RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323959535:9(9433-9447)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2023.3239595
Li YZeng FZhang NChen ZZhou LHuang MZhu TWang J(2023)Multitask Learning Using Feature Extraction Network for Smart Tourism ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2023.328132910:21(18790-18798)Online publication date: 1-Nov-2023
https://doi.org/10.1109/JIOT.2023.3281329
Kuo AChen HKu W(2023)BERT-Trip: Effective and Scalable Trip Representation using Attentive Contrast Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00053(612-623)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00053
Gao QWang WHuang LYang XLi TFujita H(2023)Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusionInformation Fusion10.1016/j.inffus.2022.11.01892:C(46-63)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.inffus.2022.11.018
Show More Cited By

View Options

Get Access

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.

Media

Figures

Other

Tables

View Table of Contents