Hybrid Filtering for Personalized and Health-Conscious Recipe Recommendations in UniEats

This research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through dashboards, and automatic grocery list generation. By enabling students to explore diverse culinary options, create balanced meal plans, and understand the nutritional content of their meals, UniEats empowers them to make informed dietary decisions. This research paper discusses the project's background, motivation, and objectives, emphasizing the importance of addressing students' dietary challenges. It also reviews existing recommendation systems and algorithms, justifying the choice of hybrid filtering for personalized meal suggestions. Additionally, the research paper details the system's design, implementation, and testing procedures, highlighting the development process. UniEats is a practical solution that leverages machine learning and data-driven methods to enhance students' culinary experiences, support skill development, and promote nutritional awareness. By tackling key challenges in meal planning and healthy eating, UniEats aims to improve the overall well-being of university students.