FPSMining: A Fast Algorithm for Mining User Preferences in Data Streams


  • Jaqueline A. J. Papini Federal University of Uberlândia
  • Sandra de Amo Federal University of Uberlândia
  • Allan Kardec S. Soares Federal University of Uberlândia




context-awareness, data mining, data streams, incremental learning, 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 mining preferences increasingly requires solutions that quickly adapt to changes. The main reason for this is that 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. The main contributions of this article are the formalization of the contextual preference mining problem in the stream setting and the introduction of two very efficient algorithms for solving this problem. We implemented both algorithms and showed their efficiency and scalability through a set of experiments over synthetic and real datasets.


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How to Cite

Papini, J. A. J., de Amo, S., & Soares, A. K. S. (2014). FPSMining: A Fast Algorithm for Mining User Preferences in Data Streams. Journal of Information and Data Management, 5(1), 4. https://doi.org/10.5753/jidm.2014.1515



SBBD 2013 Short Papers