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dc.contributor.author
Zhang, Shuai
dc.contributor.author
Tay, Yi
dc.contributor.author
Yao, Lina
dc.contributor.author
Sun, Aixin
dc.contributor.author
Zhang, Ce
dc.contributor.editor
Ricci, Francesco
dc.contributor.editor
Rokach, Lior
dc.contributor.editor
Shapira, Bracha
dc.date.accessioned
2023-06-19T11:20:43Z
dc.date.available
2023-06-03T05:33:33Z
dc.date.available
2023-06-13T08:16:42Z
dc.date.available
2023-06-19T11:20:43Z
dc.date.issued
2022-01-01
dc.identifier.isbn
978-1-0716-2196-7
en_US
dc.identifier.isbn
978-1-0716-2197-4
en_US
dc.identifier.other
10.1007/978-1-0716-2197-4_5
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/614973
dc.description.abstract
Deep neural networks have been serving as the main driving force for the emergence of cutting-edge applications in many areas including computer vision, speech recognition, natural language processing, etc. In the meantime, deep neural networks based recommender systems have demonstrated impressive abilities in performance improvements, and have led to breakthroughs in some largely underexplored tasks. Examples are recommender systems with integrated multimodal/unstructured data and temporal dynamics. This chapter provides an overview of deep neural networks based recommender systems, with two aims. One is to explain how deep neural networks can be applied to recommendation tasks and the other is to review the recent progress in this field. Specifically, we begin with basic concepts and terminologies about deep neural networks and how they are applied to recommender systems. We then present an overview of the state-of-the-art deep learning based recommendation algorithms, and discuss their strengths and limitations. Finally, we provide an outlook on the future trends and directions which might lead to the next generation of recommender systems.
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.title
Deep Learning for Recommender Systems
en_US
dc.type
Book Chapter
dc.date.published
2021-11-22
ethz.book.title
Recommender Systems Handbook
en_US
ethz.pages.start
173
en_US
ethz.pages.end
210
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2023-06-03T05:33:34Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-06-19T11:20:44Z
ethz.rosetta.lastUpdated
2023-06-19T11:20:44Z
ethz.rosetta.versionExported
true
ethz.COinS
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