Estudo Comparativo de Abordagens para Sistemas de Recomendação baseados em Personalidade com uso do serviço IBM Watson Personality Insights

In this study, we analyzed whether, with advances in personality detection (inference without using questionnaires), Collaborative Filtering approaches based on personality continue to improve the accuracy of traditional approaches (based essentially on ratings) The results indicated the possibility of improving accuracy by employing an approach using inferred data concerning any personality models analyzed (Big Five, Needs, and Values) [ ]the Values model provided results equivalent to the Big Five model (without facets), and, in general terms, there was no improvement when using the Big Five model with data from its facets (nor when including data from the other models) Keywords: Recommender Systems;Collaborative Filtering;Personality-based Recommendation;Watson Personality Insights 1 Introducao Ha pesquisadores, a exemplo de Nunes & Hu (2012) e Tkalcic (2018), indicando que a personalidade humana relaciona-se a tomada de decisoes e as preferencias do individuo e, portanto, deve ser considerada no projeto de Sistemas de Recomendacao O modelo Big Five e tambem denominado FFM (Five-Factor Model) ou OCEAN (acronimo, em ingles, dos cinco tracos do modelo: