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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Content-Boosted Restricted Boltzmann Machine for Recommendation
verfasst von : Yongqi Liu, Qiuli Tong, Zhao Du, Lantao Hu
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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Collaborative filtering and Content-based filtering methods are two famous methods used by recommender systems. Restricted Boltzmann Machine(RBM) model rivals the best collaborative filtering methods, 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 missing 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.