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Fairness and Sustainability in Multistakeholder Tourism Recommender Systems

Published: 19 June 2023 Publication History

Abstract

In the travel industry, Tourism Recommender Systems (TRS) are gaining popularity as they simplify trip planning for travelers by offering personalized recommendations for accommodations, activities, destinations, and more. Ensuring fairness in TRS involves considering the needs and viewpoints of different stakeholders, including consumers, item providers, the platform, and society. Although previous research has focused on fairness in TRS from a multistakeholder perspective, little attention has been given to generating sustainable recommendations.
This doctoral thesis introduces the concept of Societal Fairness (S-Fairness) to consider the impact of tourism on non-participating stakeholders (society) such as residents, who may be affected by tourism issues such as increased housing prices, environmental pollution, and traffic congestion. The objective of this research is to contribute to the field of TRS by (1) modeling sustainability for societal fairness, (2) developing a fair multistakeholder TRS that balances sustainability concerns with other stakeholders while minimizing trade-offs, and (3) evaluating the approach through user studies and offline dataset evaluation to ensure user acceptance of recommendations.

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Cited By

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  • (2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
  • (2024)To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel RecommendationsSN Computer Science10.1007/s42979-024-02667-x5:4Online publication date: 27-Mar-2024
  • (2023)Recommender systems for sustainability: overview and research issuesFrontiers in Big Data10.3389/fdata.2023.12845116Online publication date: 30-Oct-2023

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      cover image ACM Conferences
      UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
      June 2023
      333 pages
      ISBN:9781450399326
      DOI:10.1145/3565472
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      Published: 19 June 2023

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      Author Tags

      1. Fairness
      2. Information Retrieval
      3. Multistakeholder Recommendations
      4. Tourism Recommender Systems
      5. Travel

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      • (2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
      • (2024)To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel RecommendationsSN Computer Science10.1007/s42979-024-02667-x5:4Online publication date: 27-Mar-2024
      • (2023)Recommender systems for sustainability: overview and research issuesFrontiers in Big Data10.3389/fdata.2023.12845116Online publication date: 30-Oct-2023

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