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 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). With this purpose, the Multi-Agent Vizier Recommendation Framework (Vizier) is proposed; on the one hand, 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, in order to offer a solution that hopefully provides better adapted/personalized results than current solutions by considering the multidimensionality of users, items and context. In order to validate Vizier, ZoundBeat was implemented. ZoundBeat is a functional application of a music player that is capable of invoking the proposed framework to offer its users song recommendations.