Thesis and Dissertation Workshop (WTDBD)
Bayesian Networks, Concept Drift, Context-awareness, Data Mining, Data Streams, Preference Mining
The traditional preference mining setting has been widely studied in the literature in recent years. However, 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 this work, we address the problem of mining contextual preferences in a 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 traditional setting. As main contribution of this work, we formalize the contextual preference mining problem in the stream setting and propose appropriate algorithms for solving this problem.
Jaqueline Aparecida Jorge Papini, Sandra de Amo