Probabilistic collaborative representation learning for personalized item recommendation

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 guid...

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Main Authors: SALAH, Aghiles, LAUW, Hady W.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4240
https://ink.library.smu.edu.sg/context/sis_research/article/5243/viewcontent/uai18.pdf
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spelling sg-smu-ink.sis_research-52432020-03-24T05:53:22Z Probabilistic collaborative representation learning for personalized item recommendation SALAH, Aghiles LAUW, Hady W. 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. 2018-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4240 https://ink.library.smu.edu.sg/context/sis_research/article/5243/viewcontent/uai18.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Approximate inference Collaborative representations Generative model Latent variable Multi-layered Real-world datasets Shopping carts Variational algorithms Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Approximate inference
Collaborative representations
Generative model
Latent variable
Multi-layered
Real-world datasets
Shopping carts
Variational algorithms
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Approximate inference
Collaborative representations
Generative model
Latent variable
Multi-layered
Real-world datasets
Shopping carts
Variational algorithms
Databases and Information Systems
Numerical Analysis and Scientific Computing
SALAH, Aghiles
LAUW, Hady W.
Probabilistic collaborative representation learning for personalized item recommendation
description 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.
format text
author SALAH, Aghiles
LAUW, Hady W.
author_facet SALAH, Aghiles
LAUW, Hady W.
author_sort SALAH, Aghiles
title Probabilistic collaborative representation learning for personalized item recommendation
title_short Probabilistic collaborative representation learning for personalized item recommendation
title_full Probabilistic collaborative representation learning for personalized item recommendation
title_fullStr Probabilistic collaborative representation learning for personalized item recommendation
title_full_unstemmed Probabilistic collaborative representation learning for personalized item recommendation
title_sort probabilistic collaborative representation learning for personalized item recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4240
https://ink.library.smu.edu.sg/context/sis_research/article/5243/viewcontent/uai18.pdf
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