AI Driven Personalization: Enhancing Chef & Cuisine Recommendations with Machine Learning

Abstract - An online platform for hiring chefs provides users access to professional chefs without restrictions to their homes and any venue-required celebrations. This project brings in a chef recommendation model using machine learning to further enhance users' experience and improve the choice of chefs. A suggestion of chefs according to preferences, types of cuisine, previous hiring, and location will provide a recommendation closely tailored to user-specific needs. Collaborative filtering and trend-based recommendations self-adapt dynamically according to the interaction of users, determining popular chefs and seasonal cuisines. The real-time availability of chefs is also included in the system to make bookings more efficient. Also in place are gamification elements: reward points, badges, and leaderboards, which add course users drive to motivate chefs. Users gain points for frequent bookings and for posting reviews, while chefs earn rewards for high ratings and consistency of service. The machine learning model will be built using historical data of users, ratings of chefs, and preferences of the events to make such personal selection better, hence making the hiring experience efficient and data-driven. Revolutionizing the way a person experiences personalized dining is a product of combining artificial intelligence recommendations with an easy-to-use, hassle-free booking platform. In the future, adaptive food combination with deep learning and sophisticated behavioral analytics for chefs and users will be included.