# Ontology-Based Nutritional Recommender System
## 2 State-of-The-Art
### Dietary Recommender Systems  
Recommender systems are software applications that serve as an information filter to help users navigate large sets of alternatives and find items that fit their preferences [37; 38]. In the literature, several techniques have been proposed to predict the users' interests: (i) collaborative filtering (CF): the most widespread approach consisting of collecting preferences from many users, filtering use strategies that require collaboration, and making automatic predictions; (ii) content-based filtering (CB) utilizes features defined in items liked by users to find similar ones and suggest them; (iii) knowledge-based techniques (KBs) use implicit knowledge about users and products to infer users' needs and preferences and make the recommendations [39]; (iv) hybrid approaches that are based on a combination of the mentioned techniques. Those techniques have been widely applied in a variety of diverse domains, for instance CF in music [39], CB to retrieve learning resources [40], KB in dialogue systems [41], and hybrid approaches in dating recommender systems [42]. In particular, in the domain of this work, dietary recommendation, [PERSON] et al. [43] presented DIETOS, a dietary recommender system based on user health conditions and eventual chronic diseases. The system utilizes questionnaires to create users' health profiles and make the recommendations considering their medical conditions. In the same line, [PERSON] et al. [44] described three different recommender systems: (i) a preference-based recommender that utilizes a content-based filtering technique to only take into account the users' preferences to suggest recipes; (ii) a healthy recommender, which only considers healthy diets, ignoring users' preferences; (iii) a hybrid approach based on a combination of both previous approaches. Following the same idea about preference recommendations, [PERSON] et al. [45] developed a framework that suggests a personalized daily meal plan. The system incorporates a multi-criteria decision analysis tool for personalized meal recommendation by considering users' preferences. Similarly, [PERSON] and [PERSON] [46] proposed the Dietary Recommendation System (DRS), a system capable of analyzing users' situation, inferring whether they are following a balanced diet, and recommending suitable diet recipes to make their lives healthier. The DRS utilizes an ontology to relate nutritional information to recipes. The former is obtained from the Food Composition Database. It provides information about various nutrients, their nutritional values, and the amount that should be incorporated daily in an adult diet. The latter were extracted from two different web pages, where people publish recipes describing the ingredients and the steps to prepare the dish. Finally, [PERSON] et al. presented in [47] a recommender engine that provides personalized dietary recommendations for weightlifting athletes. The engine infers the amount of the total energy expenditure (TTE) of athletes, considering their activity level. Then, it leverages this information to recommend a balanced menu with varied foods in appropriate portions to satisfy their nutritional needs. It also uses an ontology to semantically describe specific athlete information such as weight, height, body type, training plans, food items, and nutritional values. The engine utilizes this information to execute rules expressed in the Semantic Web Rule Language (SWRL) [48] to make suggestionsconsidering the recommended energy and nutrients needed for specific training types, age, and height. In this work, we propose a recommender system that suggests recipes complying with certain diet restrictions of users depending on their health status. The recommendation engine was built on top of a domain ontology. In particular, the system analyzes the users' health situation by taking into account some of the main anthropometric measurements such as height, weight, and BMI, among others.