TripTractix: Optimizing Outing Planning with Advanced Computational Techniques

Event planning and travel applications often lack critical medical and travel-related information, despite the need to consider factors like weather and infectious disease risks, as highlighted during the COVID-19 pandemic. In Sri Lanka, there is also a scarcity of travel applications that fully utilize Natural Language Processing (NLP) to handle dynamic user requests. To bridge these gaps, this work proposes an outing application that leverages NLP to generate optimal travel recommendations. The system uses Named Entity Recognition (NER) and semantic analysis to extract keywords from user inputs, mapping activity preferences with the help of Large Language Models (LLMs) and zero-shot classification techniques. Recommendations are ranked using the TOPSIS model, while location data is integrated from the Google Place API. This approach aims to deliver personalized, data-driven outing suggestions by combining NLP, advanced recommendation algorithms, and LLMs.