Ratings Estimation on Group Recommender Systems
- Autores: Ingrid Alina Christensen, Silvia Schiaffino
- Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 15, Nº. 50, 2012 (Ejemplar dedicado a: ASAI - Argentine Symposium on Arti cial Intelligence), págs. 18-29
- Idioma: español
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Resumen
Recommender systems have been developed to find personalized content for users. The personalization techniques used in these systems, usually focus on satisfaying the needs of individual users. Nevertheless, within some domains there is a need to generate recommendations to groups of users instead of individuals. In order to detect the interest of a group of users, several aggregation techniques have been developed. A disadvantage of these techniques is that they require a large amount of computations to estimate unknown ratings. In this article, we present an analysis of the impact of estimating ratings when an aggregation technique is used. For that purpose, we describe a hybrid approach to generate group recommendations based on group modeling. We also present the results obtained when evaluating the approach and two well-known aggregation techniques in the movie domain, and the variations of those results when the estimation process is not included.
