Recommender Systems based on Personality Traits
Silveira Netto Nunes, Maria Augusta
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The World Wide Web is a great source of products and services available to people. Scientists have made a huge effort to create effective strategies to personalize those products/services for anyone willing to use them. The personalization may be provided by Recommender Systems which are able to match people’s preferences to specific products or services. Scientists from different research areas such as Psychology, Neurology and Affective Computing agree that human reasoning and decision-making are hardly ever affected by psychological aspects. Thus, to maintain the same level of personalized service provided by humans, computers should also “reason”, taking into account users’ psychological aspects. Nevertheless, the psychological aspects have, unfortunately, not been highly applied in most models of User Profiles used in Recommender Systems. As a result, the existing Recommender Systems do not actually use psychological aspects such as Personality Traits during their decision-making process in order to generate their recommendations. In this thesis we propose the implementation of the Personality Traits in User Profiles so it is possible to obtain evidence that the use of Personality Traits in Recommender Systems might be coherent and effective for the improvement of the recommendations for users and, therefore, act proactively towards users’ needs, offering more adaptable products and services according to their future needs.