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About Training Deep Autoencoders For Collaborative Filtering: Oleksii Kuchaiev Boris Ginsburg
Deep autoencoders can be used for collaborative filtering to make personalized recommendations. The paper proposes a model that significantly outperforms previous methods on a Netflix dataset. It uses a deep autoencoder with 6 layers trained end-to-end without pre-training. Experimental results show that deep autoencoders generalize better than shallow ones, negative activation functions are crucial for training deep models, and regularization like dropout is needed to prevent overfitting.
Detailed Information
| Author: | ['ĐứcĐiệu'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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