A Review of Recipe Recommendation Methods

Recipe recommendation aims to provide users with a curated list of recipes tailored to their dietary preferences, nutritional requirements, and other relevant factors. While existing recommendation algorithms have demonstrated effectiveness in traditional domains such as news, music, and product recommendations, there remains a relative paucity of research focused specifically on recipe recommendation within the culinary field, resulting in fragmented findings. The investigation into recipe recommendation methodologies primarily encompasses three key aspects: Firstly, it introduces fundamental concepts related to food computing and recommendation systems, delineates the specific research focus on recipe recommendations, and categorizes these algorithms into five distinct types: content-based methods, collaborative filtering-based methods, hybrid approaches, graph-based techniques, and nutrition- and health-oriented recommendation strategies. Secondly, it examines the sub-methods associated with each category of research methodologies while evaluating the technical advantages and limitations inherent in different approaches. Finally, it provides an overview of the current state of research concerning recipe recommendation methods and anticipates future developmental prospects in this area.