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

Lingfeng Shi, Chengkai Huang, Lina Yao +1

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…