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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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 |
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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 |
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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. |
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SALAH, Aghiles LAUW, Hady W. |
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SALAH, Aghiles LAUW, Hady W. |
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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 |
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Probabilistic collaborative representation learning for personalized item recommendation |
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probabilistic collaborative representation learning for personalized item recommendation |
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Institutional Knowledge at Singapore Management University |
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2018 |
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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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