Prediction of users’ personality traits based on task solving on the Web

Nowadays, people use web applications and systems to a greater extent than in the past. They want to gain information, realize different services or socialize. But how each user uses an application (a system), does not depend only on the user interface and user experience, but also on user himself and his personality characteristics.
Personality characteristics can help explain how users look at the webpage and how they work with it. Automatic prediction of personality characteristics could be useful for example in personalization of the applications. Specifically, in e-shop domain it could be used to recommend suitable products for each user based on the users’ personality characteristics.
In our work, we build a user model based on user’s interactions with a web page. We use this model as an input for automatic classification of user’s personality characteristics. We work with dataset from a Slovak e-shop. In this dataset there are users’ interactions with the e-shop, their purchases, ratings and views of products and many other actions. Also included in the dataset, there are completed questionnaires of these users, which reveal their personalities according to the Big Five model (5 dimensions of personality).
Besides using interaction features, we plan to extend this model and thus improve the predictions by using eye-tracking. Basic metrics of eye-tracking (number of fixations, time to first fixation, …) will be new input to the classification.