Although food and nutrition have been studied for centuries, modern nutritional science is surprisingly young. Human knowledge about food and nutrition has evolved drastically with time, and it has especially expanded the last few decades, with information and data being mass produced and available everywhere and in any form it is really easy to get overwhelmed and confused when it comes to what is right and what is wrong. Macronutrient assessment is a crucial task for individuals suffering from various diseases, and also very relevant for professional athletes, and nowadays it is becoming part of everyday life for many people, because of health or fitness reasons. Assessing the nutritional components of food is very challenging and requires reliant source of data. In this paper, we introduce an idea of finding similar recipes with regard to their macronutrient values based on learned recipe vector representation. On a scientifically proven dataset of recipe data containing description and macronutrient values we introduce word and paragraph embeddings, learn concept representations for the textual descriptions, proceed with calculating similarity between the embeddings, and then compare with the similarity between nutrient values. The results show a strong correlation between these two similarities. This study is a promising beginning for continuing in this direction – introducing multi-label classification or regression for predicting nutrient content of food.