doi:10.3850/978-981-08-7723-1_P160


Learning user Preferences for Top-k Querying - improving Learning of the Higher Ranked Objects


Alan Eckhardta and Peter Vojtášb

Department of Software Engineering, Charles University in Prague, Czech Republic.

aeckhardt@ksi.mff.cuni.cz
bvojtas@ksi.mff.cuni.cz

ABSTRACT

In this paper we deal with user preference learning to enable top-k querying. Input for learning is user overall rating of a sample set of objects. We introduce a new method which favours the higher rated objects. We present a method for evaluating the top-k query results according to this preferences favouring top of the list. We compare our method to several methods and evaluate experiments on a real data set.

Keywords: Preference learning, User preferences, Top-k querying.


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