Towards automatic problem detection in web navigation based on client-side interaction data

Nowadays the importance of digital competences is unarguable, and specially for people with functional diversity. On the other hand, the website should adapt to the user necessities automatically. This work focuses on the latter, in detecting navigation problems automatically. Firstly, the device used by the user will be detected by proposing two level hierarchy of supervised classifiers that divides in different levels errors of different criticality. Afterwards, in order to detect problems automatically, clustering algorithms and anomaly detection have been used, finding for each device a set of potential problems' indicators automatically. Moreover, the effect of cursor's adaptions in different problems has been analyzed.