DeGRe: Dense-supervised Generative Reranking for Recommendation
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 studies have shifted towards end-to-end generative frameworks, which typically leverage list-wise rewards or preference alignment to guide generator training. However, these methods still face two critical issues. First is the heuristic label bias. Existing methods often construct training targets based on simple rules, such as promoting clicked items to the top, while ignoring causal dependencies within the list context. Second is the credit assignment ...
| Authors | Chaotian Song, Jingyao Zhang, Chenghao Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 25 May 2026 |
| Last updated at source | 24 September 2026 |
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