## 4 Discussion
### Novelty Elements of the Proposed Approach and Possible Applications  
The adopted approach, if compared with the metrics found in state of the art, provides the best web page layout defined by tags estimated by an LSTM algorithm \"driven\" by UX and user score. In Table 4 are summarized some common metrics found in state of the art and in the proposed research. It is observed that the used system is potentially able to extract all the information to estimate the metric reported in the Table 4, and,  
Figure 11: UX scoring and outliers condition (L: Low; A: Average; H: High): \(\mu\) indicates the main value, and \(\sigma\) the standard deviation. g(x) is the Gaussian distribution function related to a specific UX attribute. furthermore, introduces a new metric approach. This approach is also innovative because it automatizes the construction of the training dataset, by avoiding the scores indicated by outlier conditions: only the web pages labeled with correct scores will be considered for the model training. The proposed user interface of Figure 8 is particularly useful for E-commerce web page design, where it is typically required an accurate web analytics service. The specific analysis of the mouse movements in specific areas of a web page allows understanding if there are objects of impact to be replicated in the structuration of other new web pages. In particular, the extraction of other characteristics, such as the features of a photo or of an image, could further facilitate the design process. An interesting research topic is the implicit feedback of the user, such as the sequences of viewed items (item-sequences) [37]. In this direction, a further improvement of the proposed scoring is the evaluation of information about correlations between more tags of more pages viewed in sequence and image features characterizing the visual impact (colors, shapes, etc.). The LSTM network can also be adopted to predict the user attitudes [38], anticipating the design criteria of the web page layout. Other important aspects are the clustering of web users, in order to define the structural patterns of their usage behavior [39] and the prediction of the degree of page adjustment [40]. Important analyses can also be performed by matching the user clusters with the classified web page contents in order to address the design with a narrow precision. It is also important to distinguish the web pages of interesting for a broad user from the web pages suitable for a particular user cluster (a web page for chemical synthesis products cannot be of interest for a user with a low level of training), thus addressing the design on a specific user cluster. A possible improvement of the applied research for future works can be the pre-classification and the clustering of the users to find a possible correlation with the web page tags. The results of Table 4 represent an example of a website interaction metric adopting the approach described by the flowchart diagram of Figure 3. These results support to formulate for each web page template a design criterium, which is based on the following steps:  
1. It is analyzed the final LSTM score based on UX adjustment and on the elimination of outlier conditions;
2. If the score is too low are analyzed the tags of the same web page to not consider for the realization of a new web page;
3. If the final score is high, are analyzed, and extracted the related tags concerning a major mouse interaction (movements and number of clicks); the \"high impact\" selected tags will also be adopted for the design of similar web page topics;
4. If the final score is average are analyzed only the best tags, by substituting the other ones with the best tags of similar web pages characterized by high scores. An alternative approach to model memory in the information system is the Recursive Neural Networks (RNNs), suitable for approximating the probability distribution of the outputs [41].