LLM-distilled item preferences pre-train an RL recommender, and two online adaptation schemes (fine-tuning and adaptive blending) improve cumulative rewards in simulated online recommendation.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Large Language Model driven Policy Exploration for Recommender Systems
LLM-distilled item preferences pre-train an RL recommender, and two online adaptation schemes (fine-tuning and adaptive blending) improve cumulative rewards in simulated online recommendation.