CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles
Matrix Factorization (MF) method is widely popular for personalized recommendations. However, the natural data sparsity problem limits its performance, where users generally only interact with a small fraction of available items. Accordingly, several hybrid models have been proposed recently to opti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9217428/ |