# Systematic Review of Nutritional Recommendation Systems
## 2 Material and Methods
### Review Protocol  
Firstly, the different objectives and search questions were defined (Table 1), and the following databases were used: (1) ACM was used as the main digital library of scientific content in the area of informatics and computing, (2) Scopus was taken into account as a database that has access to important different research in different areas of knowledge, and (3) PubMed was used as a more specific dataset related to the health area. No filter was used for the population filter; all available items were used. Considering the mapping of the literature, the keywords were selected; these are the words that have been used: Nutrition, Recommendation System (recommender system), and Machine Learning. A more precise search was possible due to the advanced searches of the different repositories, which enables to search using logical operators. The terms \"Recommendation System\" AND \"Nutrition\" were searched all through the full texts, AND \"Machine Learning\" just in the author keywords. Table 2 shows the records obtained for each database between the years 2016 and 2021. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Question** & **Purpose** \\ \hline Q1 & At what areas of the nutrition are & To learn about those areas in which have \\  & used the RS? & been implemented recommendation systems. \\ \hline Q2 & Do they make different kinds of & To determine the recommendations that offer \\  & nutritional recommendations? & the different platforms. \\ \hline Q3 & \
\begin{tabular}{c} What methods/equations do you \\ use to estimate the daily caloric \\ needs of users? \\ \end{tabular} & To determine if the caloric needs are agreed. \\  &
\begin{tabular}{c} What type of RS are used \\ in nutrition? \\ \end{tabular} & To determine the types of RS that are used. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research questions. The PRISMA guidelines were followed to filter the different articles from the SRL, which suggests applying four steps: (1) Identification, (2) Filtering, (3) Eligibility, and (4) Inclusion. Figure 1 represents the PRISMA-based flow chart, which shows all the details about the records that were included and excluded. In total, 214 records were obtained, and the first filter was applied. The aim of this filter was to eliminate the duplicates; in this case, there was just one text repeated in two of the databases. After this criterion, there were 213 records still available. Secondly, the second filter was applied excluding all those text that were non-English, short papers, abstracts, posters or reviews. Moreover, by the reading of the titles some of the results have also been discarded, leaving 53 records to analyze in depth. After the reading of the remaining abstracts, there were another 29 records discarded; this filter left 24 results to read. Finally, after completely reading all the remaining records, just 18 studies were included for analysis. After this analysis, a review of words related to the study that could offer another point of view to the search was carried out. Among the terms found, a new identification stage was carried out with two of these (nutrition behavior and calorie tracking) and the results of the searches can be seen in Tables 3 and 4. As a conclusion, we can observe that the search by adding the term nutrition behavior shows that most of the studies stored in ACM are related to nutrition (136). In addition, it can be seen that the Scopus database does not have any results, which shows that it is a more technical database. Finally, PubMed shows only six results (Table 3). If we look at the other search, which added the term calorie tracking, it shows similar results, although in the ACM database fewer results are obtained, since they are reduced to slightly more than half (65) (Table 4).