Abstract-To utilise the vast recipe databases on the In ternet in intelligent nutritional assistance or recommender systems, it is important to have accurate nutritional data for recipes. Unfortunately, most online recipes have no such data available or have data of suspect quality. In this pa per we present a system that automatically calculates the nutritional value of recipes sourced from the Internet. This is a challenging problem for several reasons, including lack of formulaic structure in ingredient descriptions, ingredi ent synonymy, brand names, and unspecific quantities be ing assigned. We present a system that exploits linguistic properties of ingredient descriptions and nutritional knowl edge modelled as rules to estimate the nutritional content of recipes. We evaluate the system on a large Internet sourced recipe database (23.5k recipes) and examine performance in terms of ability to recognise ingredients and error in nutri tional values against values established by human experts. Our results show that our system can match all of the in gredients for 91 % of recipes in the collection and generate nutritional values within a 10% error bound from human assessors for calorie, protein and carbohydrate values. We show that the error is less than that between multiple hu man assessors and also less than the error reported for dif ferent standard measures of estimating nutritional intake.
In (M. REF , the authors presented a system that automatically calculates the nutritional content of recipes sourced on Internet.
Ingredient matching to determine the nutritional properties of Internet-sourced recipes
2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops
2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops
Computer Science