Ling-Feng Shi

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

This paper empirically identifies two structural flaws rooted in geometric properties and dataset sparsity, and proposes a novel two-stage framework to close the long-short-view performance gap and introduces a modified softmax to counter prefix position bias.