Modeling Sustainable City Trips: Integrating CO2e Emissions, Popularity, and Seasonality into Tourism Recommender Systems
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Abstract (arXiv)
Tourism affects not only the tourism industry but also society and stakeholders such as the environment, local businesses, and residents. Tourism Recommender Systems (TRS) can be pivotal in promoting sustainable tourism by guiding travelers toward destinations with minimal negative impact. Our paper introduces a composite sustainability indicator for a city trip TRS based on the users' starting point and month of travel. This indicator integrates CO2e emissions for different transportation modes and analyses destination popularity and seasonal demand. We quantify city popularity based on user reviews, points of interest, and search trends from Tripadvisor and Google Trends data. To calculate a seasonal demand index, we leverage data from TourMIS and Airbnb. We conducted a user study to explore the fundamental trade-offs in travel decision-making and determine the weights for our proposed indicator. Finally, we demonstrate the integration of this indicator into a TRS, illustrating its ability to deliver sustainable city trip recommendations. This work lays the foundation for future research by integrating sustainability measures and contributing to responsible recommendations by TRS.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2024.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2025SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenderscited by 8
- 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generationcited by 4
- 2026TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanationscited by 1
- 2026TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations14 Apr 2026
- 2025SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders12 Apr 2025
- 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation26 Sep 2024