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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Trong Dinh Thac Do

Advanced Analytics Insitute, University of Technology Sydney


Longbing Cao

Advanced Analytics Insitute, University of Technology Sydney


DOI:

10.1609/aaai.v32i1.11689


Abstract:

Modelling sparse and large data sets is highly in demand yet challenging in recommender systems. With the computation only on the non-zero ratings, Poisson Factorization (PF) enabled by variational inference has shown its high efficiency in scalable recommendation, e.g., modeling millions of ratings. However, as PF learns the ratings by individual users on items with the Gamma distribution, it cannot capture the coupling relations between users (items) and the rating popularity (i.e., favorable rating scores that are given to one item) and rating sparsity (i.e., those users (items) with many zero ratings) for one item (user). This work proposes Coupled Poisson Factorization (CPF) to learn the couplings between users (items), and the user/item attributes (i.e., metadata) are integrated into CPF to form the Metadata-integrated CPF (mCPF) to not only handle sparse but also popular ratings in very large-scale data. Our empirical results show that the proposed models significantly outperform PF and address the key limitations in PF for scalable recommendation.

Topics: AAAI

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Trong Dinh Thac Do||Longbing Cao Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Trong Dinh Thac Do||Longbing Cao Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation AAAI 2018, .

Trong Dinh Thac Do||Longbing Cao (2018). Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Trong Dinh Thac Do||Longbing Cao. Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Trong Dinh Thac Do||Longbing Cao. 2018. Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Trong Dinh Thac Do||Longbing Cao. (2018) "Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Trong Dinh Thac Do||Longbing Cao, "Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation", AAAI, p., 2018.

Trong Dinh Thac Do||Longbing Cao. "Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Trong Dinh Thac Do||Longbing Cao. "Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Trong Dinh Thac Do||Longbing Cao. Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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