Related Experiment Video
Updated: Jul 29, 2025

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
Published on: October 6, 2020
A review on individual and multistakeholder fairness in tourism recommender systems
Ashmi Banerjee1, Paromita Banik1, Wolfgang Wörndl1
1Department of Computer Engineering, TUM School of CIT, Technical University of Munich, Munich, Germany.
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.
More Related Videos
Related Concept Videos
Stereotype Content Model
Review and Preview
Percentiles are a type of fractile that partition data into...
The Sense of Self: Reflected Self-Appraisal and Social Comparison
Social Proof
In- and Out-Groups
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

