Smart Dine-in: A Personalized Food Recommendation System

Personalization in business involves a strategy where companies gather data from their users and utilize Machine Learning techniques to provide customized content or recommendations. In this context, the paper outlines the development of an application that focuses on food recommendations tailored to individual user preferences. The application takes various user inputs into account, such as weather conditions, dietary restrictions, and the time of day. These inputs are analyzed and fed into a Machine Learning algorithm, which serves as the core decision-making component of the system. The algorithm itself operates on two primary concepts: content-based filtering and collaborative filtering. Content-based filtering assesses the characteristics and features of food items, comparing them to the user's preferences to recommend similar options. On the other hand, collaborative filtering looks at user behavior and preferences, suggesting items based on the preferences of similar users. In addition to content-based filtering and collaborative filtering, the paper also incorporates the k-nearest neighbors (KNN) algorithm as part of its recommendation system. The KNN algorithm is used to further enhance the personalization of food recommendations. It works by measuring the similarity between users based on their preferences and behaviors. Specifically, it identifies the “k” nearest users who have similar food preferences to the current user. These similar users' food choices and ratings are then analyzed to suggest food items that align with the user's taste. By employing these methods, the application generates recommendations that are highly personalized, ensuring that users receive food suggestions that align with their specific tastes and the contextual factors relevant to their meal choices. This approach can significantly enhance the user experience and increase user engagement with the application.