Vizier: un sistema de recomendación genérico y multidimensional

─ Recommender Systems have emerged to help support, augment and systematize the everyday natural social process of creating and sharing recommendations by developing tools that can be used to quickly identify interesting products, and therefore, reduce a search space of alternatives. This paper aims to present Vizier, a framework constructed under a generic approach, which provides services to Information Retrieval applications so these may offer product recommendations that consider several Adaptation/Personalization dimensions (e.g., user dimension, context, among others). On the one hand, with Vizier it is expected to assist those entities that currently develop Information Retrieval applications and wish to add recommendations to their services (e.g., E-Commerce applications). On the other hand, it is hoped to offer a solution that hopefully provides adapted/personalized results by considering the multidimensionality of users, items and context. In order to validate Vizier, ZoundBeat was created, a functional prototype of a music player that is capable of invoking the proposed framework to offer its users song recommendations.