Evaluating Natural User Preferences for Selective Retrieval

Learning user preferences is a complex area, especially difficult for performing experiments - every person is different and has different preferences, which often change in time. In this paper, we propose a method for testing a preference learning method that is in a sense more general than our previous attempts of testing an inductive method. We address the issue of limited rating set that results on larger datasets into more objects with the highest rating.