# SousChef System for Personalized Meal Recommendations: A Validation Study
## 2 Related Work
### Menu Generation Systems  
Unlike single meal recommender systems, menu generation systems create recommendations for multiple meals, comprising larger periods in the recommendations. Having extended control over the users' diet, these systems can help them follow a more balanced and diverse diet. Just like single meal recommender systems, these systems can also consider users' preferences and restrictions. A system has been developed [17] that can create daily meal plans that includes the food preferred by the user, not consumed recently, and satisfies the daily nutritional requirements. It implements a pre-filtering stage that uses AHPSort as a multi-criteria decision analysis tool for filtering out foods that are not appropriate to user's characteristics and use features for each meal-such as previous frequency, previous last consumption, and preference value-to ensure the user's preferences and diversity are met on the recommendations. Another example [15] uses a neural network to predict users' preferences on recipes based on three key factors: the users' history, recipes' list of ingredients, and descriptive images. Some systems in this category implement health-aware methods, providing a meal plan based on the user's disease needs/restrictions. In [4], an integer programming algorithm was implemented to create meal recommendations that meet the daily nutrient requirements for diabetic persons, minimizing its cost. The solution developed in [3] can recommend single, daily, or weekly meal plans, taking into account diabetics' concerns on the plan estimation but also patient's cultural, social, economic, and biological status, physical characteristics, and health concerns by using a Multi-Criteria Decision Making Approach (MCDM). Machine learning is also used in the development of meal recommender systems, as in [18] where a TensorFlow neural network model is used to classify recipe images to determine whether they are recommendable given the characteristics of a diabetic patient. There is a trend of these systems to support people with diabetes because a balanced diet has a positive impact on their well-being, but these systems can also help manage other diseases such as thyroid disorders. A support system has been implemented [19] using K-nearest neighbours and collaborative filtering models to suggest balanced diets for thyroid patients meeting their special nutrient requirements. The approaches and the problems tackled by the different studies are diverse (Table 1) and use private or hard to replicate datasets, which makes results harder to compare. The validation of the approaches often focuses on the nutritional suitability of the recommendations, disregarding other important food choice criteria. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline  & **Single Meal** & **Daily Meal** & **Week Meal** & **Disease Focus** & **Items** & **Validation Focus** \\ \hline
[8] & x & & & - & Recipes & Preferences \\ \hline
[9] & x & & & Any & Food products & Classification problem (f1 measure) \\ \hline
[10] & x & & & - & Recipes & Processing time \\ \hline
[15] & x & & & - & Recipes & Preferences \\ \hline
[16] & x & & & - & Recipes & Preferences \\ \hline
[18] & x & & & Diabetes & Recipes & Nutritional requirements \\ \hline
[19] & x & & & Thyroid & Recipes & Nutritional requirements and preferences \\ \hline
[17] & x & & - & Food items & Nutritional requirements, variability and preferences \\ \hline
[4] & & x (2 days) & & Diabetes & Recipes & Nutritional requirements and cost \\ \hline
[3] & & x & x & Diabetes & Recipes & Nutritional requirements \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related work comparing the scope of meal recommendations, the disease they are focused in, the items used in recommendations, and the focus of their validation.