Learning sparse heterogeneous user preferences

Models of user preferences will be at the core of the next generation of personalized Infor- mation Systems. We propose HPREF, an algorithm for learning a hierarchical, probabilistic preference model that integrates sparse preferences from multiple like-minded users in a principled fashion. Our preliminary experiments indicate that HPREF outperforms previous preference learning approaches and suggest several directions for further improvement.