Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Abstract

I n daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personalitybased recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem