# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 2 Materials and Methods
### Dataset Collection  
Initially, a specialized search of scientific papers from the Clarivate Web of Science and Elsevier's Scopus platforms was performed. These bibliographic databases contain information on high-quality multidisciplinary research published in scientific journals of meaningful global impact and allowed the consolidation of a dataset to contribute to this study. The search string was \"(((recommender OR recommendation) AND system) AND (tourist OR tourism OR emotion OR physiological OR affective OR wearable))\". The first part of the string refers to the recommender systems, and the second part mentions the recognition of emotions. The information was extracted from the bibliographic platforms on 15 July 2020, filters were applied to the search chain by subject (Computer Science, medicine, engineering, business, telecommunications, artificial intelligence, psychology, multidisciplinary and tourism ) and by years (2001 to 2020) A representative dataset of 1829 documents was obtained, corresponding to 33.6% from WoS and the remaining from Scopus (see Table 1).  
Figure 1: Paper organization.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Filter** & **Scopus** & **WoS** & **Documents** \\ \hline By years: Limit-to & 2001 to 2020 & 2001 to 2020 & (4308, 1623) \\ \hline By subject area: Limit-to & \begin{tabular}{l} Computer Science, Medicine, Engineering, Psychology, \\ and Business. \\ \end{tabular} & \begin{tabular}{l} Computer Science Information Systems, Artificial Intelligence, \\ Engineering, Tourism, Telecommunications, and, Psychology. \\ \end{tabular} & (3637, 570) \\ \hline By subject area: Exclude & \begin{tabular}{l} Mathematics, Social Sciences, Decision Sciences, Biochemistry, Nursing, Health, among others. \\ \end{tabular} & - & (2030, 570) \\ \hline By document type: Exclude & \begin{tabular}{l} Exclude Short Survey, Note, Editorial, and Letter. \\ \end{tabular} & - & (2016, 570) \\ \hline By language: Limit-to & \begin{tabular}{l} English \\ \end{tabular} & \begin{tabular}{l} English \\ \end{tabular} & \begin{tabular}{l} (1861, 551) \\ \end{tabular} \\ \hline By keywords: Exclude & \
\begin{tabular}{l} Human, article, priority journal, female, review, male, adult, \\ adolescent, among others. \\ \end{tabular} & - & (1303, 551) \\ \hline By source title: Exclude &
\begin{tabular}{l} Advanced Materials Research, Information Japan, Applied \\ Mechanics, among others. \\ \end{tabular} & - & (1278, 551) \\ \hline \end{tabular}
\end{table}
Table 1: Filters applied to the search string. The documents correspond to the WoS and Scopus dataset.  
The bibliographic dataset preprocessing was generated with the ScientoPy tool [37]. Table 2 shows a summary of the preprocessing of the duplicate documents that were removed from the consolidated Scopus and WoS dataset. Besides, it presents the bibliographic dataset statistical information filtered by type of documents (conference papers, articles, reviews, proceedings papers, and articles in press) and duplicate records in the DOI match. In particular, the first column of information describes the input dataset. The second column specifies the number of published documents and the number of papers resulting from the duplicate filter. Finally, the third column shows the relative percentages before and after the filter.  
The bibliographic dataset is available in the repository ([[https://github.com/luzsamtamariads4a/carswearable](https://github.com/luzsamtamariads4a/carswearable)]([https://github.com/luzsamtamariads4a/carswearable](https://github.com/luzsamtamariads4a/carswearable))), so researchers interested in this knowledge domain can use this resource.