Learning User Preferences for 2CP-Regression for a Recommender System

Authors:
Alan Eckhardt;Peter Vojtáš
Affiliations:
Department of Software Engineering, Charles University, and Institute of Computer Science, Czech Academy of Science, Prague, Czech Republic;Department of Software Engineering, Charles University, and Institute of Computer Science, Czech Academy of Science, Prague, Czech Republic
Venue:
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Year:
2009

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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 PrefWork 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.