Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations
A lightweight generative verifier for retrieval models is introduced, trained post hoc with next-token cross entropy, requires no sampled negatives or candidate pool during training, and scores only the retriever's top-$K$ candidates at inference.
Ben-Yu Zhang, Qiang Zhang, Rui Li et al.
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