Problem Statement

Travel planning is time-consuming and overwhelming. Tourists struggle to find personalized recommendations that match their specific interests, budget, and travel style.

Solution

Built an AI-driven platform that uses LLMs to conversationally understand user preferences, then applies recommendation algorithms to generate customized travel itineraries with real-time pricing and availability.

Key Features

• ▸Conversational preference elicitation using LLMs
• ▸Personalized itinerary generation
• ▸Sentiment analysis of destination reviews
• ▸Budget-aware recommendations
• ▸Interactive map-based exploration
• ▸Multi-language support

Challenges

• ⚡Balancing personalization accuracy with data privacy
• ⚡Integrating multiple data sources for comprehensive recommendations
• ⚡Handling seasonal variability in travel recommendations
• ⚡Generating diverse itineraries that avoid filter bubble effects

Results & Metrics

Lessons Learned

• 💡LLMs excel at preference elicitation through natural conversation
• 💡Hybrid recommendation approaches outperform pure collaborative filtering
• 💡User feedback loops significantly improve recommendation quality over time