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cs.IR2026
Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations
Benyu Zhang, Qiang Zhang, Rui Li +3
First-stage recommenders in multi-stage systems produce a ranked candidate list from which a limited prefix is forwarded to downstream rankers. Because each forwarded item must be…
cs.IR2026
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
Benyu Zhang, Qiang Zhang, Jianpeng Cheng +10
Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are cr…