Affiliations: Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands. E-mails: {kagie, mvanderloos, mvanwezel}@ese.eur.nl
Note: [] Corresponding author: Martijn Kagie, Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. E-mail: [email protected].
Abstract: We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model, at the cost of a very small loss in overall performance, is able to recommend new items and give an explanation of its recommendations to its users.
Keywords: Recommender systems, probabilistic latent semantic analysis, hybrid recommender systems
