ML2E: Meta-Learning Embedding Ensemble for Cold-Start Recommendation
Cold-start problem has been recognized as the most crucial challenge in recommender systems. Many recommendation algorithms work well when lots of preference information is available but start to degrade in cold-start settings. Inspired by the spirit of meta-learning, we identified that the appeal o...
| 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/9187870/ |