  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.
