Probabilistic Collaborative Representation Learning for Personalized Item Recommendation

Aghiles Salah, Hady W. Lauw
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:997-1007, 2018.

Abstract

We present Probabilistic Collaborative Repre- sentation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that rela- tionships among items within contexts (e.g., browsing session, shopping cart, etc.) may un- derlie various aspects that guide the choices people make. Intuitively, PCRL seeks repre- sentations of items reflecting various regulari- ties between them that might be useful at ex- plaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn represen- tations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learn- ing under the proposed model are challenging due to several sources of intractability. Rely- ing on the recent advances in approximate in- ference/learning, we derive an efficient varia- tional algorithm to estimate our model from observations. We further conduct experiments on several real-world datasets to showcase the benefits of the proposed model.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-salah18a, title = {Probabilistic Collaborative Representation Learning for Personalized Item Recommendation}, author = {Salah, Aghiles and Lauw, Hady W.}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {997--1007}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/salah18a/salah18a.pdf}, url = {https://proceedings.mlr.press/r16/salah18a.html}, abstract = {We present Probabilistic Collaborative Repre- sentation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that rela- tionships among items within contexts (e.g., browsing session, shopping cart, etc.) may un- derlie various aspects that guide the choices people make. Intuitively, PCRL seeks repre- sentations of items reflecting various regulari- ties between them that might be useful at ex- plaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn represen- tations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learn- ing under the proposed model are challenging due to several sources of intractability. Rely- ing on the recent advances in approximate in- ference/learning, we derive an efficient varia- tional algorithm to estimate our model from observations. We further conduct experiments on several real-world datasets to showcase the benefits of the proposed model.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Probabilistic Collaborative Representation Learning for Personalized Item Recommendation %A Aghiles Salah %A Hady W. Lauw %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-salah18a %I PMLR %P 997--1007 %U https://proceedings.mlr.press/r16/salah18a.html %V R16 %X We present Probabilistic Collaborative Repre- sentation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that rela- tionships among items within contexts (e.g., browsing session, shopping cart, etc.) may un- derlie various aspects that guide the choices people make. Intuitively, PCRL seeks repre- sentations of items reflecting various regulari- ties between them that might be useful at ex- plaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn represen- tations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learn- ing under the proposed model are challenging due to several sources of intractability. Rely- ing on the recent advances in approximate in- ference/learning, we derive an efficient varia- tional algorithm to estimate our model from observations. We further conduct experiments on several real-world datasets to showcase the benefits of the proposed model. %Z Reissued by PMLR on 04 October 2026.
APA
Salah, A. & Lauw, H.W.. (2018). Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:997-1007 Available from https://proceedings.mlr.press/r16/salah18a.html. Reissued by PMLR on 04 October 2026.

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