| Title | ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation |
| Author | |
| DOI | |
| Publication Years | 2023
|
| ISBN | 979-8-3503-0246-2
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| Source Title | |
| Pages | 908-913
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| Conference Date | 21-23 April 2023
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| Conference Place | Xi'an, China
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| Keywords | |
| SUSTech Authorship | First
|
| URL | [Source Record] |
| Data Source | IEEE
|
| PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10248832 |
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Conference paper |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/567754 |
| Department | Department of Statistics and Data Science |
| Affiliation | Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China |
| First Author Affilication | Department of Statistics and Data Science |
| First Author's First Affilication | Department of Statistics and Data Science |
| Recommended Citation GB/T 7714 |
Jingyun Feng,Qianqian Wang,Zhejun Huang,et al. ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation[C],2023:908-913.
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| Files in This Item: | There are no files associated with this item. | |||||
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