From Valid to Useful: Post-Verification Acquisition for Recursive Self-Improving Recommendation

Sequential recommenders can generate synthetic interaction sequences and retrain on the augmented corpus in a recursive self-improvement loop. To limit error accumulation, current methods verify each generated sequence remains predictive of the user's real interactions and discard those that drift a