Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, HongJiang Zhang
Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, named collaborative ensemble learning. It uses probabilistic SVMs to model each user's profile (as CBF does).At the prediction phase, it combines a society OF users profiles, represented by their respective SVM models, to predict an active users preferences(the CF idea ).The combination scheme is embedded in a probabilistic framework and retains an intuitive explanation.Moreover, collaborative ensemble learning does not require a global training stage and thus can incrementally incorporate new data.We report results based on two data sets. For the Reuters-21578 text data set, we simulate user ratings under the assumption that each user is interested in only one category. In the second experiment, we use users' opinions on a set of 642 art images that were collected through a web-based survey. For both data sets, collaborative ensemble achieved excellent performance in terms of recommendation accuracy.
PDF Link: /papers/03/p616-yu.pdf
AUTHOR = "Kai Yu
and Anton Schwaighofer and Volker Tresp and Wei-Ying Ma and HongJiang Zhang",
TITLE = "Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "616--623"