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.