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 Representation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that relationships among items within contexts (e.g., browsing session, shopping cart, etc.) may underlie various aspects that guide the choices people make. Intuitively, PCRL seeks representations of items reflecting various regularities between them that might be useful at explaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn representations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learning under the proposed model are challenging due to several sources of intractability. Relying on the recent advances in approximate inference/learning, we derive an efficient variational 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 Representation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that relationships among items within contexts (e.g., browsing session, shopping cart, etc.) may underlie various aspects that guide the choices people make. Intuitively, PCRL seeks representations of items reflecting various regularities between them that might be useful at explaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn representations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learning under the proposed model are challenging due to several sources of intractability. Relying on the recent advances in approximate inference/learning, we derive an efficient variational 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 Representation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that relationships among items within contexts (e.g., browsing session, shopping cart, etc.) may underlie various aspects that guide the choices people make. Intuitively, PCRL seeks representations of items reflecting various regularities between them that might be useful at explaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn representations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, inference and learning under the proposed model are challenging due to several sources of intractability. Relying on the recent advances in approximate inference/learning, we derive an efficient variational 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.

Related Material