User Experience in Sustainable Tourism
Revolutionizing Sustainable Tourism with AI-Powered Recommendations
Leverage Multi-Criteria Decision Making and Large Language Models for Transparent, User-Centric Travel Planning.
This analysis explores a prototype user-centered mobile tourism recommender system that integrates MCDM with LLM-based explanations. It enhances transparency and usability for sustainable tourism planning by allowing users to weight criteria like crowd levels, weather, air quality, and distance. The system uses the Borda count method for rankings and an OpenAI GPT-3.5 assistant for natural language explanations. A user study (N=13) revealed satisfactory usability, appreciation for the interactive interface, and perceived transparency. Key limitations include lack of contextual guidance for initial criteria weighting and the assistant's vulnerability to off-topic queries.
80.6
Average SUS Score
13
Participants in User Study
Satisfactory
Usability Rating
Deep Analysis & Enterprise Applications
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MCDM & LLMs
User Experience
Sustainability & Transparency
80.58
Average SUS Score (indicating good usability)
Integrating MCDM with LLMs
The system combines a simplified Multi-Criteria Decision Making (MCDM) method, specifically the Borda count, with an LLM-based natural language explanation interface. This integration aims to enhance transparency and usability in sustainable tourism planning, addressing the limitations of traditional MCDM approaches that often lack clear explanations for non-expert users.
| Feature | Traditional MCDM | AI-Enhanced MCDM |
|---|---|---|
| Transparency |
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| User Engagement |
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| Adaptability |
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| Accessibility |
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User Interaction Flow
Assign Weights to Criteria
→
Receive Personalized POI Rankings
→
Explore Interactive Map
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Consult LLM Assistant for Explanations
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Make Informed Travel Decisions
Key Findings from User Study
The user study (N=13) revealed a satisfactory average SUS score of 80.58, indicating good usability. Participants appreciated the interactive interface and the perceived transparency enabled by the LLM assistant. Real-time data integration (crowd, weather, air quality) was highly valued. However, users noted a lack of contextual guidance during initial criteria weighting and the assistant's limitations with off-topic queries.
Scenario: Oslo Tour Planning
Imagine a traveler arriving at Oslo Central Station. Using the app, they prioritize 'avoiding crowds' and 'good weather'. The system instantly recommends Vigeland Sculpture Park (Frogner Park) due to favorable weather and moderate crowd levels, providing an explanation through the LLM assistant. This allows the traveler to make a quick, informed decision aligned with their sustainable preferences.
Sustainable Criteria and Transparency
The system allows users to weight sustainability-related criteria such as crowd levels, weather conditions, air quality, and distance. This explicit integration of sustainable factors, combined with the LLM-driven transparency, aims to promote more eco-conscious travel choices. Explanations from the assistant directly reference user weights and real-time data, fostering trust and understanding.
Improved
Perceived Transparency of Recommendations
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