Short Papers
Context-awareness, Data Mining, Data Streams, Preference Mining
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 this article, 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 batch setting. As main contribution of this article, we formalize the contextual preference mining problem in the stream setting and propose an algorithm for solving this problem. We implemented this algorithm and showed its efficiency through a set of experiments over real data.
Jaqueline A. J. Papini, Sandra de Amo, Allan Kardec S. Soares