Partial VAE for Hybrid Recommender System

We propose a novel hybrid recommender system method that treats missing data in a principled manner and that uses amortized inference for fast predictions. We name this method, the Partial Variational Autoencoder (p-VAE). P-VAE uses a novel probabilistic generative model to handle varying numbers of user ratings in a principled way. Using the proposed amortized partial inference technique in p-VAEs, learning and inference can be efficiently performed by minimizing the so-called partial variational upper bound, without making ad-hoc assumptions on the values of missing ratings. Empirical experiments on the MovieLens dataset demonstrate the state-of-the-art performance of our method for movie recommendations.

[1]  Pablo M. Olmos,et al.  Handling Incomplete Heterogeneous Data using VAEs , 2018, Pattern Recognit..

[2]  Jie Yuan,et al.  Item Recommendation with Variational Autoencoders and Heterogeneous Priors , 2018, DLRS@RecSys.

[3]  M. de Rijke,et al.  A Collective Variational Autoencoder for Top-N Recommendation with Side Information , 2018, DLRS@RecSys.

[4]  Matthew D. Hoffman,et al.  Variational Autoencoders for Collaborative Filtering , 2018, WWW.

[5]  Boris Ginsburg,et al.  Training Deep AutoEncoders for Collaborative Filtering , 2017, ArXiv.

[6]  James She,et al.  Collaborative Variational Autoencoder for Recommender Systems , 2017, KDD.

[7]  Matthew D. Hoffman,et al.  Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo , 2017, ICML.

[8]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[9]  Alexander J. Smola,et al.  Deep Sets , 2017, 1703.06114.

[10]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[12]  Daniel M. Roy,et al.  Neural Network Matrix Factorization , 2015, ArXiv.

[13]  Scott Sanner,et al.  AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.

[14]  Dit-Yan Yeung,et al.  Relational Stacked Denoising Autoencoder for Tag Recommendation , 2015, AAAI.

[15]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[16]  Yoram Singer,et al.  Local Low-Rank Matrix Approximation , 2013, ICML.

[17]  Radford M. Neal.  MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.

[18]  Thore Graepel,et al.  Matchbox: large scale online bayesian recommendations , 2009, WWW '09.

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

[20]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

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

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

[23]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.