## 2 Material and Methods
### Methods: Web Page Scoring Based on Combined UX and LSTM Approaches
In Figure 4a was illustrated an explosion of the \"Web page tags\" function indicated in Figure 2, where it was possible to distinguish the different data flows involved in the data processing (source code acquisition, source code classification, HTML tag extraction, tag selection, tokenization, code conversion, LSTM model training, and LSTM model adoption). An example of tokenization processing was shown in Figure 4b. It was observed that the tokenization was also applied for other web page tags listed in Table 2, defining the features of the web page layout. The LSTM model was trained by taking into account the attributes of Table 3 and the sequence of words used in the text. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tag HTML** & **Use** & **Attributes** \\ \hline \textless{HTML}\textgreater{} & Beginning of document & // \\ \hline \textless{Title}\textgreater{} & Page title & // \\ \hline \textless{Head}\textgreater{} & Header & // \\ \hline \textless{body}\textgreater{} & Body of the page & // \\ \hline \textless{h1}\textgreater{}\textless{h6}\textgreater{} & Other headers & // \\ \hline \textless{pre}\textgreater{} & Preformatted, as written & // \\ \hline \textless{center}\textgreater{} & Centered object & // \\ \hline \textless{a href} = “url”\textgreater{}\textless{}/\textless{}/\textless{} & Hyperlink & Id, class \\ \hline \textless{img src} = “url”\textgreater{}\textless{}/\textless{}/\textless{} & Insert image & Align, alt, id, class, width, height, border \\ \hline \textless{p}\textgreater{} & Paragraph & Align \\ \hline \textless{br}\textgreater{} & Next row & // \\ \hline \textless{ulp}\textless{}\textless{}\textless{}\textgreater{} & Bulleted list and related items & Type, value, align \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main tags and attributes of a HTML web page. Figure 4: (a) Web page analysis and LSTM training approach. (b) Example of tokenization process “translating” a web page text into a sequence number. Appendix A is listed the LSTM algorithm adopted for the testing results. The LSTM model was implemented using the open-source library for deep-learning Keras [31]. The parameters analyzed for the UX are:  
* User information (device, browser);
* The time of the user remaining on a web page;
* The number of clicks \(n_{c}\);
* Mouse movements in terms of coordinates. In order to estimate these parameters has been adopted the library of [32]. The UX model indicated in Figure 2 was implemented by the following equation:  
\[s_{\text{UX}}=T^{2}.n_{c}, \tag{1}\]  
Figure 5: Tags structuring the web page layout. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tag HTML** & **Use** & **Attributes** \\ \hline \textless{}form\textgreater{} & Data entry form & Align, action, enctype \\ \hline \textless{}input\textgreater{} & Text input box & // \\ \hline \textless{}select\textgreater{} & Multiple choice box & // \\ \hline \textless{}table\textgreater{} & Table &
\begin{tabular}{c} Border, cellspacing, cellpadding, \\ width, heigh, align, bgcolor, \\ \end{tabular} \\ \hline \textless{}th\textgreater{} & Cell header & // \\ \hline \textless{}td\textgreater{} & Cell & // \\ \hline \textless{}tr\textgreater{} & Row & // \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._where \(T\) is the time spent on the web page, \(n_{c}\) is the number of clicks that the user makes on the page and \(s\) is the score that will be multiplied with the feedback given by the user. For the UX score is considered the following normalization, scaling the values between 1 and 10:  
\[s_{\text{UX}}=\frac{s_{i}-\text{min}(S)}{\text{max}(S)-\text{min}(S)}*9+1 \tag{2}\]  
where \(s_{i}\) is the \(i\)-th score and \(\text{min}(S)\) and \(\text{max}(S)\) are respectively the minimum and maximum of the score array.