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.
In: Proceedings of the Sixth ACM Conference on Recommender Systems
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.
citing papers explorer
-
Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
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.
-
HiMARS: Hybrid multi-objective algorithms for recommender systems
Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.