This research focuses on developing advanced methods for assessing similarity between recipes by combining different sources of information and analytical approaches. It explores 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, and after evaluating 318 recipe pairs, they agreed on 255 (80%). The evaluation of the experts also 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.