Scheduling on a budget: Avoiding stale recommendations with timely updates
Recommendation systems usually create static models from historical data. Due to concept drift and changes in the environment, such models are doomed to become stale, which causes their performance to degrade. In live production environments, models are therefore typically retrained at fixed time-in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827023000087 |