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A review on individual and multistakeholder fairness in tourism recommender systems

The growing use of Recommender Systems (RS) across various industries, including e-commerce, social media, news, travel, and tourism, has prompted researchers to examine these systems for any biases or fairness concerns. Fairness in RS is a multi-faceted concept ensuring fair outcomes for all stakeh...

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Detalles Bibliográficos
Autores principales: Banerjee, Ashmi, Banik, Paromita, Wörndl, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206003/
https://www.ncbi.nlm.nih.gov/pubmed/37234689
http://dx.doi.org/10.3389/fdata.2023.1168692
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author Banerjee, Ashmi
Banik, Paromita
Wörndl, Wolfgang
author_facet Banerjee, Ashmi
Banik, Paromita
Wörndl, Wolfgang
author_sort Banerjee, Ashmi
collection PubMed
description The growing use of Recommender Systems (RS) across various industries, including e-commerce, social media, news, travel, and tourism, has prompted researchers to examine these systems for any biases or fairness concerns. Fairness in RS is a multi-faceted concept ensuring fair outcomes for all stakeholders involved in the recommendation process, and its definition can vary based on the context and domain. This paper highlights the importance of evaluating RS from multiple stakeholders' perspectives, specifically focusing on Tourism Recommender Systems (TRS). Stakeholders in TRS are categorized based on their main fairness criteria, and the paper reviews state-of-the-art research on TRS fairness from various viewpoints. It also outlines the challenges, potential solutions, and research gaps in developing fair TRS. The paper concludes that designing fair TRS is a multi-dimensional process that requires consideration not only of the other stakeholders but also of the environmental impact and effects of overtourism and undertourism.
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spelling pubmed-102060032023-05-25 A review on individual and multistakeholder fairness in tourism recommender systems Banerjee, Ashmi Banik, Paromita Wörndl, Wolfgang Front Big Data Big Data The growing use of Recommender Systems (RS) across various industries, including e-commerce, social media, news, travel, and tourism, has prompted researchers to examine these systems for any biases or fairness concerns. Fairness in RS is a multi-faceted concept ensuring fair outcomes for all stakeholders involved in the recommendation process, and its definition can vary based on the context and domain. This paper highlights the importance of evaluating RS from multiple stakeholders' perspectives, specifically focusing on Tourism Recommender Systems (TRS). Stakeholders in TRS are categorized based on their main fairness criteria, and the paper reviews state-of-the-art research on TRS fairness from various viewpoints. It also outlines the challenges, potential solutions, and research gaps in developing fair TRS. The paper concludes that designing fair TRS is a multi-dimensional process that requires consideration not only of the other stakeholders but also of the environmental impact and effects of overtourism and undertourism. Frontiers Media S.A. 2023-05-10 /pmc/articles/PMC10206003/ /pubmed/37234689 http://dx.doi.org/10.3389/fdata.2023.1168692 Text en Copyright © 2023 Banerjee, Banik and Wörndl. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Banerjee, Ashmi
Banik, Paromita
Wörndl, Wolfgang
A review on individual and multistakeholder fairness in tourism recommender systems
title A review on individual and multistakeholder fairness in tourism recommender systems
title_full A review on individual and multistakeholder fairness in tourism recommender systems
title_fullStr A review on individual and multistakeholder fairness in tourism recommender systems
title_full_unstemmed A review on individual and multistakeholder fairness in tourism recommender systems
title_short A review on individual and multistakeholder fairness in tourism recommender systems
title_sort review on individual and multistakeholder fairness in tourism recommender systems
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206003/
https://www.ncbi.nlm.nih.gov/pubmed/37234689
http://dx.doi.org/10.3389/fdata.2023.1168692
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