research-article

A Comprehensive Approach to Group Recommendations in the Travel and Tourism Domain

Authors:

Julia NeidhardtAuthors Info & Claims

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 11 - 16

https://doi.org/10.1145/3099023.3099076

Published: 09 July 2017 Publication History

Abstract

The research on group recommender systems is often oversimplifying the problem of generating group recommendations, as it is usually only considering the explicit preferences of the group members and, in some cases, enriching these preferences with additional information about the individual members. In this way, an essential aspect is frequently completely neglected: the characterization of the group as an entity with a specific composition and with group-related dynamics. The goal of this paper is multifaceted, firstly, to address the limitations of state-of-the-art approaches, secondly, to describe the problem of group recommendations in a more comprehensive fashion, thirdly, to summarize the results of our previously conducted analyses as a supporting evidence of a need for richer group models, and finally, to discuss an alternative and rather novel approach to group recommendations in the tourism domain. To this end, the results of the group decision-making study with 200 participants in 55 groups are summarized and related to the seven travel factors of the picture-based recommendation system.

References

[1]

I. Ali and S.-W. Kim. 2015. Group recommendations: approaches and evaluation. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication. ACM, 105.

Digital Library

[2]

I. Alina Christensen and S. Schiaffno. 2014. Social influence in group recommender systems. Online Information Review 38, 4 (2014), 524--542.

[3]

L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. 2003. Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Applied Artificial Intelligence 17(8--9) (2003), 687--714.

[4]

P. Bekkerman, S. Kraus, and F. Ricci. 2006. Applying cooperative negotiation methodology to group recommendation problem. In Proceedings of Workshop on Recommender Systems in 17th European Conference on Artificial Intelligence. 72--75.

[5]

S. Berkovsky and J. Freyne. 2010. Group-based recipe recommendations: analysis of data aggreagation strategies. In Proceedings of the 4th ACM conference on Recommender systems. 111--118.

Digital Library

[6]

A. Delic, J. Neidhardt, N. Nguyen, F. Ricci, L. Rook, H. Werthner, and M. Zanker. 2016. Observing Group Decision Making Processes. In Proceedings of the tenth ACM conference on Recommender systems, RecSys'16.

Digital Library

[7]

A. Delic, J. Neidhardt, T. N. Nguyen, and F. Ricci. 2016. Research methods for group recommender systems. Proceedings of RecTour 2016 (2016).

[8]

A. Delic, J. Neidhardt, L. Rook, H. Werthner, and M. Zanker. 2017. Researching Individual Satisfaction with Group Decisions in Tourism: Experimental Evidence. In Information and Communication Technologies in Tourism 2017. Springer, 73--85.

[9]

A. Delic, J. Neidhardt, and H. Werthner. 2016. Are Sun Lovers nervous? - research note at ENTER 2016 eTourism conference. Bilbao, Spain.

[10]

D.R. Forsyth. 2014. Group Dynamics (6th ed.). Wadsworth Cengage Learning.

[11]

M. Gartrell, X. Xing, Q. Lv, A. Beach, R. Han, S. Mishra, and K. Seada. 2010. Enhancing group recommendation by incorporating social relationship interactions. In Proceedings of the 16th ACM international conference on Supporting group work. ACM, 97--106.

Digital Library

[12]

H. Gibson and A. Yiannakis. 2002. Tourist roles: Needs and the lifecourse. Annals of tourism research 29(2) (2002), 358--383.

[13]

L. R. Goldberg. 1990. An alternative "description of personality": the big-five factor structure. Journal of personality and social psychology 59(6) (1990), 1216.

[14]

A. Gunawardana and G. Shani. 2015. Evaluating Recommender Systems. In Recommender Systems Handbook. 265--308.

[15]

A. Jameson. 2004. More than the sum of its members: challenges for group recommender systems. In Proceedings of the working conference on Advanced visual interfaces. 48--54.

Digital Library

[16]

D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. 2010. Recommender systems: an introduction. Cambridge University Press.

Digital Library

[17]

J. Masthoff. 2015. Group recommender systems: aggregation, satisfaction and group attributes. In Recommender Systems Handbook (2nd ed.), F. Ricci, L. Rokach, and B. Shapira (Eds.). Springer, 743--776.

[18]

J. Masthoff and A. Gatt. 2006. In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. In User Modeling and User-Adapted Interaction, Vol. 16. Springer, 281--319.

Digital Library

[19]

K. McCarthy, L. McGinty, B. Smyth, and M. Salamo. 2006. The needs of the many: a case-based group recommender system. Advances in Case-Based Reasoning (2006), 196--210.

Digital Library

[20]

J. Neidhardt, R. Schuster, L. Seyfang, and H. Werthner. 2014. Eliciting the users' unknown preferences. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 2645767, 309--312.

Digital Library

[21]

J. Neidhardt, L. Seyfang, R. Schuster, and H. Werthner. 2015. A picture-based approach to recommender systems. Information Technology & Tourism 15, 1 (2015), 49--69.

[22]

T. N. Nguyen and F. Ricci. 2016. Supporting Group Decision Making with Recommendations and Explanations. In Posters, Demos, Late-breaking Results and Workshop Proceedings of the 24th Conference on User Modeling, Adaptation, and Personalization (UMAP 2016). Halifax, Canada.

[23]

T. N. Nguyen and F. Ricci. 2017. Dynamic Elicitation of User Preferences in a Chat-Based Group Recommender System. In Proceedings of the 32nd ACM Symposium on Applied Computing. 1685--1692.

Digital Library

[24]

L. Quijano-Sanchez, J. Recio-Garcia, B. Diaz-Agudo, and G. Jimenez-Diaz. 2013. Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology (TIST) 4, 1 (2013), 8.

Digital Library

[25]

E. Quintarelli, E. Rabosio, and L. Tanca. 2016. Recommending New Items to Ephemeral Groups Using Contextual User Influence. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 285--292.

Digital Library

[26]

F. Ricci, L. Rokach, and B. Shapira. 2015. Recommender Systems: Introduction and Challenges. In Recommender Systems Handbook. 1--34.

[27]

R. S. Tindale and T. Kameda. 2000. Social sharedness as a unifying theme for information processing in groups. Group Processes and Intergroup Relations 3, 2 (2000), 123--140.

[28]

M. Tkalcic and L. Chen. 2015. Personality and recommender systems. In Recommender systems handbook. Springer, 715--739.

[29]

H. Werthner and F. Ricci. 2004. E-commerce and tourism. Commun. ACM 47, 12 (2004), 101--105.

Digital Library

[30]

V. F. Wood and P. A. Bell. 2008. Predicting interpersonal conflict resolution styles from personality characteristics. Personality and Individual Differences 45, 2 (2008), 126--131.

Cited By

Lu MZhu PMiao CWang YWang J(2024)Group Recommendation Method Based on Graph Neural Network2024 International Conference on Cyber-Physical Social Intelligence (ICCSI)10.1109/ICCSI62669.2024.10799321(1-6)Online publication date: 8-Nov-2024
https://doi.org/10.1109/ICCSI62669.2024.10799321
Streviniotis EChalkiadakis G(2022)Preference Aggregation Mechanisms for a Tourism-Oriented Bayesian RecommenderPRIMA 2022: Principles and Practice of Multi-Agent Systems10.1007/978-3-031-21203-1_20(331-346)Online publication date: 12-Nov-2022
https://doi.org/10.1007/978-3-031-21203-1_20
Calbimonte JMartin SCalvaresi DCotting A(2021)A Platform for Difficulty Assessment and Recommendation of Hiking TrailsInformation and Communication Technologies in Tourism 202110.1007/978-3-030-65785-7_9(109-122)Online publication date: 12-Jan-2021
https://doi.org/10.1007/978-3-030-65785-7_9
Show More Cited By

Index Terms

A Comprehensive Approach to Group Recommendations in the Travel and Tourism Domain
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
      2. User studies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Group recommender systems for tourism: how does personality predict preferences for attractions, travel motivations, preferences and concerns?
Abstract
To travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is ...
Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such ...
Social group recommendation in the tourism domain

Recommender Systems learn users' preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

456 pages

ISBN:9781450350679

DOI:10.1145/3099023

Editors:
Marko Tkalcic
Free University of Bolzano
,
Dhaval Thakker
University of Leeds
,
Panagiotis Germanakos
SAP SE & University of Cyprus
,
Kalina Yacef
University of Sydney
,
Cecile Paris
CSIRO ICT Centre
,
Olga Santos
Spanish National University for Distance Education
,
General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Michel Desmarais
Polytechnique University, Montreal, Canada
,
Federica Cena
University of Torino, Italy

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 the author(s) 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: 09 July 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UMAP '17

Sponsor:

UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
289
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lu MZhu PMiao CWang YWang J(2024)Group Recommendation Method Based on Graph Neural Network2024 International Conference on Cyber-Physical Social Intelligence (ICCSI)10.1109/ICCSI62669.2024.10799321(1-6)Online publication date: 8-Nov-2024
https://doi.org/10.1109/ICCSI62669.2024.10799321
Streviniotis EChalkiadakis G(2022)Preference Aggregation Mechanisms for a Tourism-Oriented Bayesian RecommenderPRIMA 2022: Principles and Practice of Multi-Agent Systems10.1007/978-3-031-21203-1_20(331-346)Online publication date: 12-Nov-2022
https://doi.org/10.1007/978-3-031-21203-1_20
Calbimonte JMartin SCalvaresi DCotting A(2021)A Platform for Difficulty Assessment and Recommendation of Hiking TrailsInformation and Communication Technologies in Tourism 202110.1007/978-3-030-65785-7_9(109-122)Online publication date: 12-Jan-2021
https://doi.org/10.1007/978-3-030-65785-7_9
Chaudhari KThakkar A(2019)A Comprehensive Survey on Travel Recommender SystemsArchives of Computational Methods in Engineering10.1007/s11831-019-09363-7Online publication date: 9-Oct-2019
https://doi.org/10.1007/s11831-019-09363-7
Calbimonte JMartin SCalvaresi DZappelaz NCotting A(2019)Semantic Data Models for Hiking Trail Difficulty AssessmentInformation and Communication Technologies in Tourism 202010.1007/978-3-030-36737-4_24(295-306)Online publication date: 17-Dec-2019
https://doi.org/10.1007/978-3-030-36737-4_24
Delic AMasthoff JWerthner H(2019)The Effects of Group Diversity in Group Decision-Making Process in the Travel and Tourism DomainInformation and Communication Technologies in Tourism 202010.1007/978-3-030-36737-4_10(117-129)Online publication date: 17-Dec-2019
https://doi.org/10.1007/978-3-030-36737-4_10
Delic AMasthoff JMitrovic TZhang JChen LChin D(2018)Group Recommender SystemsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209272(377-378)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209272
Delic AMasthoff JNeidhardt JWerthner HMitrovic TZhang JChen LChin D(2018)How to Use Social Relationships in Group RecommendersProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209226(121-129)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209226
Delic ANeidhardt JNguyen TRicci F(2018)An observational user study for group recommender systems in the tourism domainInformation Technology & Tourism10.1007/s40558-018-0106-y19:1-4(87-116)Online publication date: 19-Feb-2018
https://doi.org/10.1007/s40558-018-0106-y
Nguyen TRicci F(2017)A chat-based group recommender system for tourismInformation Technology & Tourism10.1007/s40558-017-0099-y18:1-4(5-28)Online publication date: 13-Dec-2017
https://doi.org/10.1007/s40558-017-0099-y

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