FeastAl: An ML & LLM-powered dinner selection web application

Abstract

In today’s fast-paced Restaurant Industry of Bangladesh, discovering the perfect diner has become an experience that goes beyond the basic nature of searching; that is why a personalized and intelligent recommendation system has become essential to help Bangladeshi users to come up with an intuitive and e!ective way of searching restaurants. This project introduces a comprehensive restaurant recommendation engine that uses advanced machine learning algorithms and large language models (LLMs) to find the best eating options for each user. By assessing crucial characteristics such as geographical proximity, the system narrows down options based on the user’s distance from probable restaurants, assuring convenience. After that, it uses sophisticated sentiment analysis and rate evaluation algorithms to analyze restaurant reviews, providing unbiased information on the caliber of the cuisine and the level of service. Furthermore, the engine incorporates user history and behavior tracking, learning from previous choices and dining patterns to recommend restaurants that match individual tastes. The unique time-based rating feature of this project ensures that suggestions are both enticing and useful by balancing the user’s available eating time with the restaurant’s food preparation time. In order to customize recommendations to each user’s unique time limitations and culinary tastes, the system also looks into connections between food type and preparation time. The ultimate result is a strong, data-driven recommendation system that dynamically aligns high-quality dining experiences with both personal preferences and practical logistical issues.