Content-Boosted Restricted Boltzmann Machine for Recommendation

Collaborative filtering and Content-based filtering methods are two famous methods used by recommender systems. Restricted Boltz- mann Machine(RBM) model rivals the best collaborative filtering meth- ods, but it focuses on modeling the correlation between item ratings. In this paper, we extend RBM model by incorporating content-based features such as user demograohic information, items categorization and other features. We use Naive Bayes classifier to approximate the miss- ing entries in the user-item rating matrix, and then apply the modified UI-RBM on the denser rating matrix. We present expermental results that show how our approach, Content-boosted Restricted Boltzmann Machine(CB-RBM), performs better than a pure RBM model and other content-boosted collaborative filtering methods.

[1]  Taghi M. Khoshgoftaar,et al.  Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[2]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[3]  Christian Posse,et al.  Bayesian Mixed-Effects Models for Recommender Systems , 1999 .

[4]  Adam Prügel-Bennett,et al.  Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering , 2010 .

[5]  Taghi M. Khoshgoftaar,et al.  A Mixture Imputation-Boosted Collaborative Filter , 2008, FLAIRS.

[6]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[7]  Konstantinos G. Margaritis,et al.  Collaborative Filtering through SVD-Based and Hierarchical Nonlinear PCA , 2010, ICANN.

[8]  Thomas D. Nielsen,et al.  A latent model for collaborative filtering , 2012, Int. J. Approx. Reason..

[9]  Lazaros S. Iliadis,et al.  Artificial Neural Networks - ICANN 2010 - 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I , 2010, International Conference on Artificial Neural Networks.

[10]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[11]  Bong-Jin Yum,et al.  Collaborative filtering based on iterative principal component analysis , 2005, Expert Syst. Appl..

[12]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[13]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[14]  Mu Zhu,et al.  Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation , 2011, RecSys '11.

[15]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[16]  Adam Prügel-Bennett,et al.  Kernel-Mapping Recommender system algorithms , 2012, Inf. Sci..

[17]  Preslav Nakov,et al.  A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines , 2013, ICML.