OCARM uses teacher-student distillation to let retention models learn from inaccessible post-conversion content without feature leakage, yielding improvements in offline experiments and online A/B tests.
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Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling
OCARM uses teacher-student distillation to let retention models learn from inaccessible post-conversion content without feature leakage, yielding improvements in offline experiments and online A/B tests.