research-article

Recommending a Sequence of Points of Interest to a Group of Users in a Mobile Context

Author:

Daniel HerzogAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 402 - 406

https://doi.org/10.1145/3109859.3109860

Published: 27 August 2017 Publication History

Abstract

Recommender systems (RSs) recommend points of interest (POIs), such as restaurants, museums or monuments, to users. In practice, tourists often travel in groups and want to visit a sequence of POIs along an enjoyable route. Recommending such a sequence of items to a group complicates the problem of travel recommendations because the preferences of all group members have to be taken into account. In this work, we want to examine how a RS can solve the so-called Tourist Trip Design Problem (TTDP) for a group of users. We present the most important components of our work and the research questions we want to be answered. We summarize the results we achieved so far and outline future work.

References

[1]

Florian Alt, Stefan Schneegaß, Albrecht Schmidt, Jörg Müller, and Nemanja Memarovic. 2012. How to Evaluate Public Displays. In Proceedings of the 2012 International Symposium on Pervasive Displays (PerDis '12). ACM, New York, NY, USA, Article 17, 6 pages.

Digital Library

[2]

Liliana Ardissono, Anna Goy, Giovanna Petrone, Marino Segnan, and Pietro Torasso. 2002. Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups. In Revised Papers from the International Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity. Springer-Verlag, London, UK, UK, 280--295. http://dl.acm.org/citation.cfm?id= 648105.746696

Digital Library

[3]

John Brooke. 1996. SUS-A quick and dirty usability scale. Usability evaluation in industry (1996), 189--194.

[4]

Sérgio R. de M. Queiroz and Francisco de A.T. de Carvalho. 2004. Making Collaborative Group Recommendations Based on Modal Symbolic Data. In Advances in Artificial Intelligence -- SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings, Ana L. C. Bazzan and Sofiane Labidi (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 307--316.

[5]

Thomas A Feo and Mauricio G.C Resende. 1989. A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8, 2 (1989), 67 -- 71.

Digital Library

[6]

Damianos Gavalas, Michael Kenteris, Charalampos Konstantopoulos, and Grammati Pantziou. 2012. Web application for recommending personalised mobile tourist routes. Software, IET 6, 4 (2012), 313--322.

[7]

Daniel Herzog and Wolfgang Wörndl. 2016. Exploiting Item Dependencies to Improve Tourist Trip Recommendations. In Proceedings of the Workshop on Recommenders in Tourism co-located with 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, MA, USA, September 15, 2016. 55--58. http://ceur-ws.org/Vol-1685/paper10.pdf

[8]

Anthony Jameson. 2004. More Than the Sum of Its Members: Challenges for Group Recommender Systems. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI '04). ACM, New York, NY, USA, 48--54.

Digital Library

[9]

Anthony Jameson and Barry Smyth. 2007. Recommendation to Groups. In The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Springer, Berlin, Heidelberg, 596--627. http://dl.acm.org/citation.cfm?id=1768197.1768221

Digital Library

[10]

Bart P Knijnenburg and Martijn C Willemsen. 2015. Evaluating recommender systems with user experiments. In Recommender Systems Handbook. Springer, 309--352.

[11]

Christopher Laß, Wolfgang Wörndl, and Daniel Herzog. 2016. A Multi-Tier Web Service and Mobile Client for City Trip Recommendations. In The 8th EAI International Conference on Mobile Computing, Applications and Services (MobiCASE). ACM.

Digital Library

[12]

Judith Masthoff. 2004. Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. User Modeling and User-Adapted Interaction 14, 1 (Feb. 2004), 37--85.

Digital Library

[13]

Judith Masthoff. 2015. Group Recommender Systems: Aggregation, Satisfaction and Group Attributes. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer US, Boston, MA, 743--776.

[14]

Kevin McCarthy, Lorraine McGinty, Barry Smyth, and Maria Salamó. 2006. The Needs of the Many: A Case-based Group Recommender System. In Proceedings of the 8th European Conference on Advances in Case-Based Reasoning (ECCBR'06). Springer-Verlag, Berlin, Heidelberg, 196--210.

Digital Library

[15]

Thuy Ngoc Nguyen and Francesco Ricci. 2016. Supporting Group Decision Making with Recommendations and Explanations. In Late-breaking Results, Posters, Demos, Doctoral Consortium and Workshops Proceedings of the 24th ACM Conference on User Modeling, Adaptation and Personalisation (UMAP 2016), Halifax, Canada, July 13--16, 2016. http://ceur-ws.org/Vol-1618/Poster2.pdf

[16]

Francesco Ricci. 2002. Travel Recommender Systems. IEEE Intelligent Systems (2002), 55--57.

[17]

Pieter Vansteenwegen, Wouter Soufiriau, Greet Vanden Berghe, and Dirk Van Oudheusden. 2011. The City Trip Planner. Expert Syst. Appl. 38, 6 (June 2011), 6540--6546.

Digital Library

[18]

Pieter Vansteenwegen and Dirk Van Oudheusden. 2007. The mobile tourist guide: an OR opportunity. OR Insight 20, 3 (2007), 21--27.

[19]

Adriano Venturini and Francesco Ricci. 2006. Applying Trip@Dvice Recommendation Technology to www.visiteurope.com. In Proceedings of the 2006 Conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva Del Garda, Italy. IOS Press, Amsterdam, The Netherlands, The Netherlands, 607--611. http://dl.acm.org/citation.cfm?id=1567016.1567148

Digital Library

[20]

Wolfgang Wörndl, Alexander Hefele, and Daniel Herzog. 2017. Recommending a sequence of interesting places for tourist trips. Information Technology & Tourism (2017), 1--24.

[21]

M. Xie, L. V. S. Lakshmanan, and P. T. Wood. 2011. CompRec-Trip: A composite recommendation system for travel planning. In 2011 IEEE 27th International Conference on Data Engineering. 1352--1355.

Digital Library

Cited By

Li JZhang ZSong DYang BZhou L(2025)Cruise onboard itinerary planning for multi passengers with service venue capacity and time-window constraintsComputers and Operations Research10.1016/j.cor.2024.106944176:COnline publication date: 20-Feb-2025
https://dl.acm.org/doi/10.1016/j.cor.2024.106944
Zhang JMa MGao XChen G(2024)Encoder-Decoder Based Route Generation Model for Flexible Travel RecommendationIEEE Transactions on Services Computing10.1109/TSC.2024.337623117:3(905-920)Online publication date: May-2024
https://doi.org/10.1109/TSC.2024.3376231
Pargal SDas DSahoo BMitra BChakraborty S(2023)GRIDS: Personalized Guideline Recommendations while Driving Through a New CityACM Transactions on Recommender Systems10.1145/36093372:2(1-28)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3609337
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Index Terms

Recommending a Sequence of Points of Interest to a Group of Users in a Mobile Context
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Citations

Cited By

Li JZhang ZSong DYang BZhou L(2025)Cruise onboard itinerary planning for multi passengers with service venue capacity and time-window constraintsComputers and Operations Research10.1016/j.cor.2024.106944176:COnline publication date: 20-Feb-2025
https://dl.acm.org/doi/10.1016/j.cor.2024.106944
Zhang JMa MGao XChen G(2024)Encoder-Decoder Based Route Generation Model for Flexible Travel RecommendationIEEE Transactions on Services Computing10.1109/TSC.2024.337623117:3(905-920)Online publication date: May-2024
https://doi.org/10.1109/TSC.2024.3376231
Pargal SDas DSahoo BMitra BChakraborty S(2023)GRIDS: Personalized Guideline Recommendations while Driving Through a New CityACM Transactions on Recommender Systems10.1145/36093372:2(1-28)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3609337
Jadhav SKothari DGori DJoshi DMehta B(2023)PlanIT: An Itinerary Planner using K-Means Clustering and Travelling Salesman Problem2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA)10.1109/ICCUBEA58933.2023.10392185(1-4)Online publication date: 18-Aug-2023
https://doi.org/10.1109/ICCUBEA58933.2023.10392185
Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555161
Lu XLiu JGan SLi TXiao YLiu Y(2022)Recommendation Model Based on Dynamic Interest Group Identification and Data CompensationIEEE Transactions on Network and Service Management10.1109/TNSM.2021.311270219:1(89-99)Online publication date: Mar-2022
https://doi.org/10.1109/TNSM.2021.3112702
Yang CZhang JGao XChen G(2021)MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation2021 IEEE International Conference on Web Services (ICWS)10.1109/ICWS53863.2021.00030(145-154)Online publication date: Sep-2021
https://doi.org/10.1109/ICWS53863.2021.00030
Santamaria-Granados LMendoza-Moreno JRamirez-Gonzalez G(2020)Tourist Recommender Systems Based on Emotion Recognition—A Scientometric ReviewFuture Internet10.3390/fi1301000213:1(2)Online publication date: 24-Dec-2020
https://doi.org/10.3390/fi13010002
Huang TGong YZhang YZhan ZZhang J(2020)Automatic Planning of Multiple Itineraries: A Niching Genetic Evolution ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.293922421:10(4225-4240)Online publication date: Oct-2020
https://doi.org/10.1109/TITS.2019.2939224
Tenemaza MLujan-Mora SDe Antonio ARamirez J(2020)Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization ProposalIEEE Access10.1109/ACCESS.2020.29903488(79003-79023)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2990348
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