A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems
In most recommender systems, the data of user feedbacks are usually represented with a set of discrete values, which are difficult to exactly describe users’ interests. This problem makes it not easy to exactly model users’ latent preferences for recommendation. Intuitively, a basic idea for this is...
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| Published in: | Neurocomputing (Amsterdam) Vol. 332; pp. 137 - 148 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
07.03.2019
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| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
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