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TripMate is an AI-powered travel recommendation platform designed to provide personalized travel suggestions based on user preferences and behavior. I developed the system using collaborative filtering, NLP-based aspect analysis, and emotion recognition to improve the personalization of recommendations. The application was built with Python and Django, with machine learning and deep learning models integrated into the recommendation workflow.
Collaborative filtering can work well once a traveler has history, but the first session is the hard one. Do you use a short preference setup to handle that cold start, or rely on context from the search?
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This is really nice