Mining Preferences from Superior and Inferior Examples

Mining user preferences plays a critical role in many impor- tant applications such as customer relationship management (CRM), product and service recommendation, and market- ing campaigns. In this paper, we identify an interesting and practical problem of mining user preferences: in a multidi- mensional space where the user preferences on some cate- gorical attributes are unknown, from some superior and in- ferior examples provided by a user, can we learn about the user's preferences on those categorical attributes? We model the problem systematically and show that mining user pref- erences from superior and inferior examples is challenging. Although the problem has great potential in practice, to the best of our knowledge, it has not been explored systemat- ically before. As the ¯rst attempt to tackle the problem, we propose a greedy method and show that our method is practical using real data sets and synthetic data sets.
Date: August 02, 2008
Book Title: ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'08)
Type: Proceedings
Address: Las Vegas, NV, USA
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Bibtex


@Proceedings{Mining_Preferences_from_Superior_and_Inf,
  author = "Bin Jiang and Jian Pei and Xuemin Lin and David W. Cheung and Jiawei Han",
  title = "{Mining Preferences from Superior and Inferior Examples}",
  month = "August",
  year = "2008",
  address = ", Las Vegas, NV, USA",
  booktitle = "ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'08)",
}