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https://hdl.handle.net/2440/134899
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dc.contributor.author | Xiao, T. | - |
dc.contributor.author | Tian, H. | - |
dc.contributor.author | Shen, H. | - |
dc.contributor.editor | Yang, Q. | - |
dc.contributor.editor | Zhou, Z.H. | - |
dc.contributor.editor | Gong, Z. | - |
dc.contributor.editor | Zhang, M.L. | - |
dc.contributor.editor | Huang, S.J. | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Lecture Notes in Artificial Intelligence, 2019 / Yang, Q., Zhou, Z.H., Gong, Z., Zhang, M.L., Huang, S.J. (ed./s), vol.11439, pp.426-437 | - |
dc.identifier.isbn | 9783030161477 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | https://hdl.handle.net/2440/134899 | - |
dc.description.abstract | In this paper, we propose a Variational Deep Collaborative Matrix Factorization (VDCMF) algorithm for social recommendation that infers latent factors more effectively than existing methods by incorporating users’ social trust information and items’ content information into a unified generative framework. Unlike neural network-based algorithms, our model is not only effective in capturing the non-linearity among correlated variables but also powerful in predicting missing values under the robust collaborative inference. Specifically, we use variational auto-encoder to extract the latent representations of content and then incorporate them into traditional social trust factorization. We propose an efficient expectation-maximization inference algorithm to learn the model’s parameters and approximate the posteriors of latent factors. Experiments on two sparse datasets show that our VDCMF significantly outperforms major state-of-the-art CF methods for recommendation accuracy on common metrics. | - |
dc.description.statementofresponsibility | Teng Xiao, Hui Tian, and Hong Shen | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.relation.ispartofseries | Lecture Notes in Artificial Intelligence | - |
dc.rights | © Springer Nature Switzerland AG 2019 | - |
dc.source.uri | https://link.springer.com/ | - |
dc.subject | Recommender System; Matrix Factorization; Deep Learning; Generative mode | - |
dc.title | Variational deep collaborative matrix factorization for social recommendation | - |
dc.type | Conference paper | - |
dc.contributor.conference | Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (14 Apr 2019 - 17 Apr 2019 : Macau, China) | - |
dc.identifier.doi | 10.1007/978-3-030-16148-4_33 | - |
dc.publisher.place | Switzerland | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP150104871 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shen, H. [0000-0002-3663-6591] [0000-0003-0649-0648] | - |
Appears in Collections: | Computer Science publications |
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