Item Category Aware Conditional Restricted Boltzmann Machine Based Recommendation

Though Collaborative Filtering is one of most effective recommendation technique, the problem of dealing sparsity brings traditional collaborative filtering recommendation systems great challenge. In this paper, we propose an improved Item Category aware Conditional Restricted Boltzmann Machine Frame model for recommendation by integrating item category information as the conditional layer, aiming to optimise the model parameters, so as to get better recommendation efficiency. Experimental studies on the standard benchmark datasets of MovieLens 100i?źk and MovieLens 1i?źM have shown its potential in improving recommendation accuracy.

[1]  Zhang Xiong,et al.  Autoencoder-Based Collaborative Filtering , 2014, ICONIP.

[2]  Fillia Makedon,et al.  Using singular value decomposition approximation for collaborative filtering , 2005, Seventh IEEE International Conference on E-Commerce Technology (CEC'05).

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

[4]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

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

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

[7]  Ferda Nur Alpaslan,et al.  Using Social Networks to Solve Data Sparsity Problem in One-Class Collaborative Filtering , 2010, 2010 Seventh International Conference on Information Technology: New Generations.

[8]  Zhong Yao,et al.  Integrating Item Category Information in Collaborative Filtering Recommender Algorithm , 2008, 2008 Fourth International Conference on Natural Computation.

[9]  Debajyoti Mukhopadhyay,et al.  Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey , 2015, ArXiv.

[10]  Thomas D. Nielsen,et al.  Scalable learning of probabilistic latent models for collaborative filtering , 2015, Decis. Support Syst..

[11]  Dunja Mladenic,et al.  Data Sparsity Issues in the Collaborative Filtering Framework , 2005, WEBKDD.

[12]  Konstantinos G. Margaritis,et al.  Using SVD and demographic data for the enhancement of generalized Collaborative Filtering , 2007, Inf. Sci..

[13]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..