Abstract
Contemporary information systems are facing challenging tasks involving advanced data analysis, pattern discovery, and knowledge utilization. Data mining can be successfully employed to sieve through huge amounts of raw data in search for interesting patterns. Knowledge discovered during data mining activity can be used to provide value-added services to users, customers, and organizations.
The adoption of the Web as one of the main media for business-to-customer (B2C) communication provides novel opportunities for using data mining to personalize and enhance customer interfaces. In this paper we introduce the notion of recommendation rules — a simple knowledge model that can be successfully used in the Web environment to improve the quality of B2C relationship by highly personalized communication. We present the formalism and we show how to efficiently generate recommendation rules from a large body of customer data.
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© 2005 Springer-Verlag Berlin Heidelberg
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Morzy, M. (2005). Recommendation Rules — a Data Mining Tool to Enhance Business-to-Customer Communication in Web Applications. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_52
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DOI: https://doi.org/10.1007/3-540-32392-9_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25056-2
Online ISBN: 978-3-540-32392-1
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