Exponential reward weighting with a tuned temperature improves offline generative recommenders, and a new theory decomposes its suboptimality into coverage and noise costs that predict the observed inverted-U in performance.
World Wide Web , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Exponential reward weighting with a tuned temperature improves offline generative recommenders, and a new theory decomposes its suboptimality into coverage and noise costs that predict the observed inverted-U in performance.