Learning user preferences for top-k querying - improving learning of the higher ranked objects

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