Strategies for Mining User Preferences in a Data Stream Setting

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 paper, we formally introduce 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. Besides the formalization of the contextual preference mining problem in the stream setting, we propose two strategies for solving this problem. As a first attempt to evaluate these strategies, we implemented one of them, the greedy one, and showed its efficiency through a set of experiments over real data.