DeGRe: Dense-supervised Generative Reranking for Recommendation
arXiv:2605.25749v2 Announce Type: replace-cross Abstract: In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring optimal sequences within an exponentially large permutation space. Recent…