Análisis, diseño e implementación de un sistema adaptivo de recomendación de información basado en mashups

Abstract

Currently, the growth of the Web has led people to have at their disposal more information than can be analyzed. This causes information overloading, which can be avoided with alternative solutions such as using a recommender system. The aim of this paper is to explore the field of recommender systems and it summarizes analysis, design and implementation of an adaptive recommender system based on mashups. The system serves as an add-on to Mozilla Firefox browser and provides recommendations of music, videos and websites. Finally, the system provides an interface for recommendations’ notification and, according to how it was designed, it can be adapted to new types of recommendations in a way that does not only include the three topics exposed in this work, moreover, it was implemented using Web services, which facilitates its further use by various types of interface such as a Web or desktop application.