Publication at Faculty of Mathematics and Physics |
2010
Abstract
In this paper we deal with a task to learn a general user model from user ratings of a small set of objects. This general model is used to recommend top-k objects to the user.
We consider several (also some new) alternatives of learning local preferences and several alternatives of aggregation (with or without 2CP-regression). The main contributions are evaluation of experiments on our prototype tool Pref-Work with respect to several satisfaction measures and the proposal of method Peak for normalisation of numerical attributes.
Our main objective is to keep the number of sample data which the user has to rate reasonable small.