ADER
Continual adaption of recommendation systems without forgetting
Recommendation systems typically require continual adaptation to take into account new and obsolete items. A major challenge in this situation is catastrophic forgetting, where the trained model forgets patterns it has learned before. We propose a method to mitigate this effect.
inactive
—
entered showcase: 2021-11-05
—
entry updated: 2024-03-20
This project has not yet been evaluated by the C4DT Factory team.
We will be happy to evaluate it upon request.
Simulation
Python
MIT