Mineração de preferências contextuais em data streams

Abstract

The traditional preference mining setting, referred to here as the batch setting, has been widely studied in the literature in recent years. However, the dynamic nature of the problem of mining preferences increasingly requires solutions that quickly adapt to change. The main reason for this is that frequently user\'s preferences are not static and can evolve over time. In the work described in this master\'s thesis, we address the problem of mining contextual preferences in the data stream setting. Contextual Preferences have been recently treated in the literature and some methods for mining this special kind of preferences have been proposed in the batch setting. As main contributions of the work described in this master\'s thesis, we formalize the contextual preference mining problem in the data stream setting and propose three algorithms for solving this problem. In addition, we also propose a formalism about concept drift in contextual preferences. We have implemented two of the proposed algorithms and showed their eciency through an extensive set of experiments over real and synthetic data (with and without concept drift).