Template-Type: ReDIF-Paper 1.0
Author-Name: Kagie, M.
Author-Name-Last: Kagie
Author-Name-First: Martijn
Author-Person: pka404
Author-Name: van der Loos, M.J.H.M.
Author-Name-Last: van der Loos
Author-Name-First: Matthijs
Author-Name: van Wezel, M.C.
Author-Name-Last: van Wezel
Author-Name-First: Michiel
Title: Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering
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 is competitive to other recommenders and can be used to explain the recommendations to the users.
Creation-Date: 2008-08-27
File-URL: https://repub.eur.nl/pub/13180/ERS-2008-053-MKT.pdf
File-Format: application/pdf
Series: RePEc:ems:eureri
Number: ERS-2008-053-MKT
Classification-JEL: C44, C63, M, M31
Keywords: algorithms, hybrid recommender systems, probabilistic latent semantic analysis, recommender systems
Handle: RePEc:ems:eureri:13180