Leveraging clustering to improve collaborative filtering
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Extensive work on matrix factorization (MF) techniques have been done recently as they provide accurate rating prediction models in recommendation systems. Additional extensions, such as neighbour-aware models, have been shown to improve rating prediction further. However, these models often suffer from a long computation time. In this paper, we propose a novel method that applies clustering algorithms to the latent vectors of users and items. Our method can capture the common interests between the cluster of users and the cluster of items in a latent space. A matrix factorization technique is then applied to this cluster-level rating matrix to predict the future cluster-level interests. We then aggregate the traditional user-item rating predictions with our cluster-level rating predictions to improve the rating prediction accuracy. Our method is a general “wrapper” that can be applied to all collaborative filtering methods. In our experiments, we show that our new approach, when applied to a variety of existing matrix factorization techniques, improves their rating predictions and also results in better rating predictions for cold-start users. Above all, in this paper we show that better quality and more quantity of these clusters achieve a better rating prediction accuracy.
KeywordsCollaborative filtering Recommendation system Matrix factorization
This work was made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca) and Compute/Calcul Canada. The authors would like to thank the reviewers of the 2013 ACM Recommender System conference (RecSys’13) for their valuable comments.
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