Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

Martijn Kagie (martijn.kagie@calcasa.nl), Matthijs van der Loos and Michiel van Wezel

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

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.

Keywords: algorithms; hybrid recommender systems; probabilistic latent semantic analysis; recommender systems (search for similar items in EconPapers)
JEL-codes: C44 C63 M M31 (search for similar items in EconPapers)
Date: 2008-08-27
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Citations: View citations in EconPapers (1)

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