Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2018

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.

Keywords

Approximate inference, Collaborative representations, Generative model, Latent variable, Multi-layered, Real-world datasets, Shopping carts, Variational algorithms

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Uncertainty in Artificial Intelligence (UAI 2018): Monterey, CA, August 6-10: Proceedings

First Page

998

Last Page

1008

ISBN

9781510871601

Publisher

AUAI Press

City or Country

Corvallis, OR

Copyright Owner and License

Authors

Additional URL

http://auai.org/uai2018/proceedings/papers/354.pdf

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