A Characterization Methodology of Evolutionary Behavior in Recommender Systems

Recommender Systems (RSs) have become increasingly important tools for various commercial applications on the Web. Despite numerous efforts, RSs still require improvements to make recommendation more effective and applicable to many real scenarios. Recent studies point out the temporal evolution as a primordial manner for improving RSs without, however, understand in detail how this evolution emerges. Thus, we propose a methodology for evolutive characterization of users and applications in order to provide a better understanding of this temporal dynamic in RSs. Applying our methodology in a real scenario has proved to be useful even to help in the choice of RSs adherents of each scenario.

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