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
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CSTS learns context-dependent weights for multiple objectives in a multi-objective contextual bandit and outperforms fixed-weight and standard contextual bandit baselines on Swiss public broadcaster programming data.
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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.