DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
Neural re-ranking in multi-stage recommender systems: A review.arXiv preprint arXiv:2202.06602, 2022
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RecoChain unifies generative candidate generation via hierarchical semantic IDs and SIM-based ranking in a single Transformer to improve top-K recommendation performance.
PermR approximates constrained ILP revenue maximization via neighbor swaps, reaching 63% of optimal gains within latency bounds and delivering 2% revenue lift in a 56M-query online test.
Dual-Rerank fuses autoregressive and non-autoregressive generative reranking via knowledge distillation and uses list-wise decoupled RL optimization to improve whole-page utility and cut latency in industrial video search.
citing papers explorer
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DeGRe: Dense-supervised Generative Reranking for Recommendation
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation
RecoChain unifies generative candidate generation via hierarchical semantic IDs and SIM-based ranking in a single Transformer to improve top-K recommendation performance.
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Fast and Feasible: Permutation-based Constrained Reranking for Revenue Maximization
PermR approximates constrained ILP revenue maximization via neighbor swaps, reaching 63% of optimal gains within latency bounds and delivering 2% revenue lift in a 56M-query online test.
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Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking
Dual-Rerank fuses autoregressive and non-autoregressive generative reranking via knowledge distillation and uses list-wise decoupled RL optimization to improve whole-page utility and cut latency in industrial video search.