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      A review on individual and multistakeholder fairness in tourism recommender systems

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          Abstract

          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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          COVID-19 and China’s Hotel Industry: Impacts, a Disaster Management Framework, and Post-Pandemic Agenda

          Highlights • A comprehensive review of the impact of COVID-19 on China’s hotel industry. • A COVID-19 management framework which addresses the anti-pandemic phases, principles, and strategies. • And four Post-COVID-19 strategies including multi-business and multi-channels, product design and investment preference, digital and intelligent transformation, and market reshuffle.
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            Fairness through awareness

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              Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment

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                Author and article information

                Contributors
                Journal
                Front Big Data
                Front Big Data
                Front. Big Data
                Frontiers in Big Data
                Frontiers Media S.A.
                2624-909X
                10 May 2023
                2023
                : 6
                : 1168692
                Affiliations
                Department of Computer Engineering, TUM School of CIT, Technical University of Munich , Munich, Germany
                Author notes

                Edited by: Emanuel Lacić, Know Center, Austria

                Reviewed by: Marko Tkalcic, University of Primorska, Slovenia; Jürgen Ziegler, University of Duisburg-Essen, Germany; Tomislav Duricic, Graz University of Technology, Austria

                *Correspondence: Ashmi Banerjee ashmi.banerjee@ 123456tum.de
                Article
                10.3389/fdata.2023.1168692
                10206003
                bba4fe70-be7c-42d8-893c-da9e1ebeb509
                Copyright © 2023 Banerjee, Banik and Wörndl.

                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.

                History
                : 17 February 2023
                : 18 April 2023
                Page count
                Figures: 2, Tables: 2, Equations: 0, References: 127, Pages: 17, Words: 14316
                Categories
                Big Data
                Review
                Custom metadata
                Recommender Systems

                tourism recommender systems,travel,information retrieval,multistakeholder recommendations,fairness

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