An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score

Alessandro Massaro,Daniele Giannone,Angelo Maurizio Galiano,Vitangelo Birardi

doi:10.3390/fi13060145

Abstract

The proposed paper introduces an innovative methodology useful to assign intelligent scores to web pages. The approach is based on the simultaneous use of User eXperience (UX), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms, providing the web page scoring and taking into account outlier conditions to construct the training dataset. Specifically, the UX tool analyses different parameters addressing the score, such as navigation time, number of clicks, and mouse movements for page, finding possible outliers, the ANN are able to predict outliers, and the LSTM processes the web pages tags together with UX and user scores. The final web page score is assigned by the LSTM model corrected by the UX output and improved by the navigation user score. This final score is useful for the designer by suggesting the tags typologies structuring a new web page layout of a specific topic. By using the proposed methodology, the web designer is addressed to allocate contents in the web page layout. The work has been developed within a framework of an industry project oriented on the formulation of an innovative AI interface for web designers.

Highlights

Many approaches about the analysis of the usability of an internet site are based on the check of usage patterns through log files or on the analysis of static pages according to criteria defined by the web designer
By applying the Dropout technique [35], where randomly selected neurons are ignored during training, it is guaranteed a correct generalization of the system in processing new source codes not previously used
By comparing the training and testing plots, it was observed that the training epoch number to achieve the best results was higher if compared with the testing epoch number: this was due to the initial training of the Long Short-Term Memory (LSTM) network and was a function of the complexity of the web page

Summary

Introduction

Many approaches about the analysis of the usability of an internet site are based on the check of usage patterns through log files or on the analysis of static pages according to criteria defined by the web designer. Concerning E-Commerce pages, Artificial Intelligence (AI) algorithms play an important role in shaping consumer demand [18,19] and defining factors influencing the related use [20]. Other methods such as the UX click rates [20] are interesting to formulate recommendation models in E-Commerce. Other metrics and methods suitable for web analysis are discussed in [29] Following this direction, has been formulated a platform, which automatically provides the best web page template to consider addressing the design. The paper describes the architecture of an innovative platform based on the concepts previously discussed and suggesting a model to construct and design a web page

Results

Discussion

Conclusion