Conditional Restricted Boltzmann Machines for Cold Start Recommendations

Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold start problems for there is no rating for a new item. In this paper, we first apply conditional RBM (CRBM) which could take extra information into account and show that CRBM could solve cold start problem very well, especially for rating prediction task. CRBM naturally combine the content and collaborative data under a single framework which could be fitted effectively. Experiments show that CRBM can be compared favourably with matrix factorization models, while hidden features learned from the former models are more easy to be interpreted.

[1]  Geoffrey E. Hinton,et al.  Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.

[2]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[3]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[4]  Peter V. Gehler,et al.  The rate adapting poisson model for information retrieval and object recognition , 2006, ICML.

[5]  Nitish Srivastava,et al.  Modeling Documents with Deep Boltzmann Machines , 2013, UAI.

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

[7]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[8]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[9]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[10]  Deepak Agarwal,et al.  Regression-based latent factor models , 2009, KDD.

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

[12]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[13]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[14]  Jie Tang,et al.  Addressing cold start in recommender systems: a semi-supervised co-training algorithm , 2014, SIGIR.

[15]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[16]  Tat-Seng Chua,et al.  Addressing cold-start in app recommendation: latent user models constructed from twitter followers , 2013, SIGIR.

[17]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[18]  Lars Schmidt-Thieme,et al.  Learning Attribute-to-Feature Mappings for Cold-Start Recommendations , 2010, 2010 IEEE International Conference on Data Mining.

[19]  Christopher Meek,et al.  Tied boltzmann machines for cold start recommendations , 2008, RecSys '08.

[20]  Wei Chu,et al.  Information Services]: Web-based services , 2022 .

[21]  Geoffrey E. Hinton,et al.  To recognize shapes, first learn to generate images. , 2007, Progress in brain research.

[22]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[23]  Mingxuan Sun,et al.  Learning multiple-question decision trees for cold-start recommendation , 2013, WSDM.

[24]  Geoffrey E. Hinton,et al.  Learning Multilevel Distributed Representations for High-Dimensional Sequences , 2007, AISTATS.

[25]  Yoshua Bengio,et al.  Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.

[26]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[27]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

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