PERSONALITYML: A MARKUP LANGUAGE TO STANDARDIZE THE USER PERSONALITY IN RECOMMENDER SYSTEMS
Revista Gestão Inovação e Tecnologias
PERSONALITYML: A MARKUP LANGUAGE TO STANDARDIZE THE USER PERSONALITY IN RECOMMENDER SYSTEMS
Autor Correspondente: Maria Augusta Silveira Netto Nunes | [email protected]
Palavras-chave: User modeling, Personalization, Personality-based recommender systems, PersonalityML, Recommender inputs
Resumos Cadastrados
Resumo Inglês:
In recent years the study of how human psychological aspects may improve the decision-making
process in computers has became a new trend. This subject has attracted the attention from both
academy and industry in areas such as human-computer interaction, computer in education,
recommender systems and social matching systems, among others. However, one of the biggest
problems faced by them is how effectively to use, model and implement those psychological aspects
in computers. This paper comes to fill partly this gap by proposing a markup language to
standardize the representation of personality. The PersonalityML proposes a set of recommender
inputs to be used as starting data to classical cold-start problem in recommender systems, as well
as, in personality-based recommender systems and others personality-based web applications.
