Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
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Abstract (arXiv)
This position paper summarizes our published review on individual and multistakeholder fairness in Tourism Recommender Systems (TRS). Recently, there has been growing attention to fairness considerations in recommender systems (RS). It has been acknowledged in research that fairness in RS is often closely tied to the presence of multiple stakeholders, such as end users, item providers, and platforms, as it raises concerns for the fair treatment of all parties involved. Hence, fairness in RS is a multi-faceted concept that requires consideration of the perspectives and needs of the different stakeholders to ensure fair outcomes for them. However, there may often be instances where achieving the goals of one stakeholder could conflict with those of another, resulting in trade-offs. In this paper, we emphasized addressing the unique challenges of ensuring fairness in RS within the tourism domain. We aimed to discuss potential strategies for mitigating the aforementioned challenges and examine the applicability of solutions from other domains to tackle fairness issues in tourism. By exploring cross-domain approaches and strategies for incorporating S-Fairness, we can uncover valuable insights and determine how these solutions can be adapted and implemented effectively in the context of tourism to enhance fairness in RS.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2025Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourismcited by 7
- 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generationcited by 4
- 2025Point of Interest Recommendation: Pitfalls and Viable Solutionscited by 0
- 2026Multi-Dimensional Evaluation of Sustainable City Trips with LLM-as-a-Judge and Human-in-the-Loopcited by 0
- 2026Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systemscited by 0
- 2026Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems4 May 2026
- 2026Multi-Dimensional Evaluation of Sustainable City Trips with LLM-as-a-Judge and Human-in-the-Loop27 Apr 2026
- 2025Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism20 Aug 2025
- 2025Point of Interest Recommendation: Pitfalls and Viable Solutions18 Jul 2025
- 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation26 Sep 2024