A Comparative Study of a Hybrid Recommender System Merging Collaborative and Content-Based Strategies for Nutritional Tailoring in Children

This research analyzes the execution of a hybrid recommender system that merges collaborative and content-based filtering techniques to convey individualized nutritional recommendations for kids. Content-based filtering systems suggest items based on their characteristics, such as accessibility and particular elements. Collaborative filtering technique examine customer's choice to recommend suitable items based on similarity. Using a single recommendation can be challenging, the purpose of research study is to compare the effectiveness of content-based filtering, collaborative filtering and their combination. The combination of these techniques looks to make a more accurate recommendation system that customizes nutrition instructions to the dietary preferences and necessity of individual children. By exploring the potential benefits of hybrid recommender systems in real-world scenarios, this research focuses on capacity and flexibility to diverse customer profiles. This research contributes appreciated insights into enhancing child nutrition recommendation system, giving a robust approach to offering customized dietary guidance to parents.