A two-phase data construction framework generates explanatory rationales from user feedback and applies uncertainty-based distillation to fine-tune lightweight LLMs as preference-aligned user simulators for recommender systems.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
CPGRec+ improves game recommendations on Steam data by reweighting player-game edges with signed preference strengths and using LLMs to generate preference-aware descriptions, yielding higher accuracy and diversity than prior models.
citing papers explorer
-
CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
CPGRec+ improves game recommendations on Steam data by reweighting player-game edges with signed preference strengths and using LLMs to generate preference-aware descriptions, yielding higher accuracy and diversity than prior models.