Affective-recommender: um sistema de recomendação sensível ao estado afetivo do usuário

• Autor(a) principal: Pereira, Adriano
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• dARK ID: ark:/26339/001300000rjwr
• Texto Completo: http://repositorio.ufsm.br/handle/1/5406
• topic: Computação afetiva; Sistema de recomendação; Sistema de recomendação sensível ao contexto; Computação ubíqua; E-learning; Affective computing; Recommendation system; Context-aware recommendation; System; Ubiquitous computing; E-learning; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Pervasive computing systems aim to improve human-computer interaction, using users situation variables that define context. The boom of Internet makes growing availables items to choose, giving cost in made decision process. Affective Computing has in its goals to identify user s affective/emotional state in a computing interaction, in order to respond to it automatically. Recommendation systems help made decision selecting and suggesting items in scenarios where there are huge information volume, using, traditionally, users prefferences data. This process could be enhanced using context information (as physical, environmental or social), rising the Context-Aware Recommendation Systems. Due to emotions importance in our lives, that could be treated with Affective Computing, this work uses affective context as context variable, in recommendation process, proposing the Affective-Recommender a recommendation system that uses user s affective state to select and to suggest items. The system s model has four components: (i) detector, that identifies affective-state, using the multidimesional Pleasure, Arousal and Dominance model, and Self-Assessment Maniking instrument, that asks user to inform how he/she feels; (ii) recommender, that selects and suggests items, using a collaborative-filtering based approache, in which user s prefference to an item is his/her affective reaction to it as the affective state detected after access; (iii) application, which interacts with user, shows probable most interesting items defined by recommender, and requests affect identification when it is necessarly; and (iv) data base, that stores available items and users prefferences. As a use case, Affective-Recommender is used in a e-learning scenario, due to personalization obtained with recommendation and emotion importances in learning process. The system was implemented over Moodle LMS. To exposes its operation, a use scenario was organized, simulating recommendation process. In order to check system applicability, with students opinion about to inform how he/she feels and to receive suggestions, it was applied in three UFSM graduation courses classes, and then it were analyzed data access and the answers to a sent questionnaire. As results, it was perceived that students were able to inform how they feel, and that occured changes in their affecive state, based on accessed item, although they don t see improvements with the recommendation, due to small data available to process and showr time of application.