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Modeling Tourists' Personality in Recommender Systems: How Does Personality Influence Preferences for Tourist Attractions?

Authors:

Patrícia Alves,

João Carneiro,

Helena Martins,

Goreti MarreirosAuthors Info & Claims

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Pages 4 - 13

https://doi.org/10.1145/3340631.3394843

Published: 13 July 2020 Publication History

Abstract

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality.

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Cited By

Moisa DParapanos DHeap T(2025)The Role of Digital Footprints for Destination Competitiveness and Engagement: Utilizing Mobile Technology for Tourist Segmentation Integrating Personality TraitsInternational Journal of Tourism Research10.1002/jtr.7000627:1Online publication date: 20-Feb-2025
https://doi.org/10.1002/jtr.70006
Zhang ZWei ZMa L(2024)UBRMTC: User Behavior Recognition Model With Transaction CharacterIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325722711:2(1589-1601)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3257227
Waris MZaman Fakhar MGulsoy MYalcin EBilge A(2024)A Novel Pre-Processing Technique to Combat Popularity Bias in Personality-Aware Recommender SystemsIEEE Access10.1109/ACCESS.2024.351047512(183230-183251)Online publication date: 2024
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Index Terms

Modeling Tourists' Personality in Recommender Systems: How Does Personality Influence Preferences for Tourist Attractions?
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User centered design
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

426 pages

ISBN:9781450368612

DOI:10.1145/3340631

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

Copyright © 2020 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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Published: 13 July 2020

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July 14 - 17, 2020

Genoa, Italy

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Moisa DParapanos DHeap T(2025)The Role of Digital Footprints for Destination Competitiveness and Engagement: Utilizing Mobile Technology for Tourist Segmentation Integrating Personality TraitsInternational Journal of Tourism Research10.1002/jtr.7000627:1Online publication date: 20-Feb-2025
https://doi.org/10.1002/jtr.70006
Zhang ZWei ZMa L(2024)UBRMTC: User Behavior Recognition Model With Transaction CharacterIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325722711:2(1589-1601)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3257227
Waris MZaman Fakhar MGulsoy MYalcin EBilge A(2024)A Novel Pre-Processing Technique to Combat Popularity Bias in Personality-Aware Recommender SystemsIEEE Access10.1109/ACCESS.2024.351047512(183230-183251)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3510475
Alves PMartins ANegrão FNovais PAlmeida AMarreiros G(2024)Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systemsExpert Systems with Applications10.1016/j.eswa.2024.124812255(124812)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124812
Ozcelik SYondem MCaetano IFigueiredo JAlves PMarreiros GBahtiyar HYuksel EPerales FSuciu G(2023)Transforming Tourism Experience: AI-Based Smart Travel PlatformProceedings of the 4th European Symposium on Software Engineering10.1145/3651640.3651645(37-45)Online publication date: 1-Dec-2023
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Cena FConsole LLikavec SMicheli MVernero F(2023)How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young TravellersIEEE Access10.1109/ACCESS.2023.328525811(61968-61985)Online publication date: 2023
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Alves PMartins HSaraiva PCarneiro JNovais PMarreiros G(2023)Group recommender systems for tourism: how does personality predict preferences for attractions, travel motivations, preferences and concerns?User Modeling and User-Adapted Interaction10.1007/s11257-023-09361-233:5(1141-1210)Online publication date: 15-May-2023
https://doi.org/10.1007/s11257-023-09361-2
Amato A(2023)Recommender Systems in the Museum Sector: An OverviewAdvanced Information Networking and Applications10.1007/978-3-031-28694-0_23(251-260)Online publication date: 15-Mar-2023
https://doi.org/10.1007/978-3-031-28694-0_23
Blanco JGe MPitner T(2023)An Adaptive Filter for Preference Fine-Tuning in Recommender SystemsWeb Information Systems and Technologies10.1007/978-3-031-24197-0_7(107-121)Online publication date: 18-Jan-2023
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Alves PGomes DRodrigues CCarneiro JNovais PMarreiros G(2022)Grouplanner: A Group Recommender System for Tourism with Multi-agent MicroServicesAdvances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection10.1007/978-3-031-18192-4_37(454-460)Online publication date: 13-Oct-2022
https://doi.org/10.1007/978-3-031-18192-4_37
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