2 papers
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.IR2025
PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong +20
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit signi…