Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation
This work addresses recipe similarity estimation for the food industry, supporting personalized diets and automated systems, but it is incremental as it builds on existing analytical approaches.
This research tackled the problem of assessing similarity between recipes by combining semantic, lexical, and domain perspectives, resulting in expert agreement on 80% of 318 evaluated recipe pairs.
This research focuses on developing advanced methods for assessing similarity between recipes by combining different sources of information and analytical approaches. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes. A web-based interface was developed to allow domain experts to validate the combined similarity results. After evaluating 318 recipe pairs, experts agreed on 255 (80%). The evaluation of expert assessments enables the estimation of which similarity aspects--lexical, semantic, or nutritional--are most influential in expert decision-making. The application of these methods has broad implications in the food industry and supports the development of personalized diets, nutrition recommendations, and automated recipe generation systems.