Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes

Understand

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.