SMART Restaurant Recommender: A ContextAware Restaurant Recommendation Engine

With the rise of ecommerce systems and web application usage, recommendation systems have become important to our daily tasks. They provide personalized suggestions to assist with any task under consideration. While various machine learning algorithms have been developed for recommendation tasks, existing systems still face limitations. This research focuses on advancing contextaware recommendation systems by leveraging the capabilities of Large Language Models (LLMs) in conjunction with realtime data. The research exploits the integration of existing realtime data APIs with LLMs to enhance the capabilities of the recommendation systems already integrated into smart societies. The experimental results demonstrate that the hybrid approach significantly improves the user experience and recommendation quality, ensuring more relevant and dynamic suggestions.