Devoloping a Robust and Accurate Food Recommendation System Using a Hybrid Filtering Approach

Abstract

With the increase of fame for online food platforms as well as a broad range of culinary choices, there has been a need for stronger and more correct food recommendation systems that can help users in discovering new and fascinating foods that are tailored to their individual tastes. This paper presents an innovative design of constructing a recommendation system by utilizing both content-based approach and collaborative filtering techniques. Our system applies machine learning algorithms to examine user preferences as well as dish attributes with personalized recommendations based on it thereby increasing satisfaction levels and overall engagement rates. The experimental results we provide herein demonstrate the efficacy and accuracy of our hybrid filtering method and prove its ability to transform how individuals find pleasure in eating.