Ling-Feng Shi

Closing the Long-Short View Gap in Sequential Recommendation without Cached History

Sequential recommenders are typically trained on long user histories to capture rich behavioral signals, yet serving with training-length sequences is often impractical due to real-time efficiency constraints. Directly using only recent behaviors leads to a severe performance drop. To bridge this gap, existing approach...