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The first step in the development of a framework that allows the comparison of different recommender systems, configurations and ways of implementing them. Additionally, this thesis promotes the sharing of the results of these comparisons and proposes a unified method of representing the comparison.

Clustering. --- Collaborative filtering. --- Coverage. --- Half-life utility. --- Item-based. --- Mean absolute error. --- Pre-processed. --- Program. --- Recommender system. --- User-based.