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

TL;DR

This paper introduces a probabilistic hybrid filtering method called collaborative ensemble learning that unifies collaborative and content-based filtering, allowing incremental updates and improved recommendation accuracy.

Contribution

It proposes a novel probabilistic framework combining user profiles modeled by SVMs with collaborative filtering, enabling incremental learning without a global training stage.

Findings

Achieved high recommendation accuracy on text and image datasets.

Effectively combines user profiles for improved predictions.

Supports incremental data incorporation without retraining.

Abstract

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.