Bayesian Nonparametric Poisson Factorization for Recommendation Systems

Prem Gopalan, Francisco J. Ruiz, Rajesh Ranganath, David Blei
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:275-283, 2014.

Abstract

We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an efficient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with fixed dimensionality. We studied our model and algorithm with large real-world data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-gopalan14, title = {{Bayesian Nonparametric Poisson Factorization for Recommendation Systems}}, author = {Gopalan, Prem and Ruiz, Francisco J. and Ranganath, Rajesh and Blei, David}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {275--283}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/gopalan14.pdf}, url = {https://proceedings.mlr.press/v33/gopalan14.html}, abstract = {We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an efficient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with fixed dimensionality. We studied our model and algorithm with large real-world data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.} }
Endnote
%0 Conference Paper %T Bayesian Nonparametric Poisson Factorization for Recommendation Systems %A Prem Gopalan %A Francisco J. Ruiz %A Rajesh Ranganath %A David Blei %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-gopalan14 %I PMLR %P 275--283 %U https://proceedings.mlr.press/v33/gopalan14.html %V 33 %X We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an efficient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with fixed dimensionality. We studied our model and algorithm with large real-world data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.
RIS
TY - CPAPER TI - Bayesian Nonparametric Poisson Factorization for Recommendation Systems AU - Prem Gopalan AU - Francisco J. Ruiz AU - Rajesh Ranganath AU - David Blei BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-gopalan14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 275 EP - 283 L1 - http://proceedings.mlr.press/v33/gopalan14.pdf UR - https://proceedings.mlr.press/v33/gopalan14.html AB - We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an efficient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with fixed dimensionality. We studied our model and algorithm with large real-world data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart. ER -
APA
Gopalan, P., Ruiz, F.J., Ranganath, R. & Blei, D.. (2014). Bayesian Nonparametric Poisson Factorization for Recommendation Systems. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:275-283 Available from https://proceedings.mlr.press/v33/gopalan14.html.

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