Comparative Analysis of Machine Learning Models for Menu Recommendation System

In an era characterized by an unprecedented abundance of food-related content and a growing diversity of user preferences, personalized recommendation systems play a vital role in enhancing user experiences across various online platforms. This research paper studies multiple single models to be combined within an Ensemble Model in order to provide more accurate and diversified recommendations in the future works. The primary objective of this study is to evaluate and compare the performance of four distinct models – SVM, Random Forest, LSTM RNN, and Collaborative Filtering, considering precision and recall as key evaluation metrics, in order to identify the most effective approach for enhancing user experience and increasing customer satisfaction. Through rigorous tests and performance evaluation, we analyze the strengths and weaknesses of each model in terms of recommendation accuracy, scalability, and real-world applicability. Furthermore, this study creates a foundation for upcoming work by proposing the combination of the most efficient models within an Ensemble Model. By harnessing the collective capabilities of these diverse models, we are planning to build a powerful recommendation system and improve recommendation quality.