arXiv:2609.36740v1 Announce Type: new
Abstract: Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Lear...
By Ren Kishimoto, Koichi Tanaka, Haruka Kiyohara, Yusuke Narita, Yasuo Yamamoto, Nobuyuki Shimizu, Yuta Saito
The paper introduces Adaptive Doubly Robust (ADR), an off‑policy evaluation method for ranking policies that blends adaptive importance weighting with reward regression to reduce variance. ADR is unbiased when the true user behavior model is known and, under a sufficient condition, achieves lower variance than the prior Adaptive Inverse Propensity Scoring (AIPS) approach. Experiments on synthetic data show that ADR consistently improves mean squared error over AIPS and other ranking OPE estimators across various data sizes and ranking lengths.
By Kosuke Iguchi, Ren Kishimoto
arXiv:2607. 20655v1 Announce Type: cross Abstract: Lead ranking in Customer Relationship Management (CRM) systems faces a persistent challenge: models achieving high offline accuracy often underperform in production.
By Chenyu Zhang
arXiv:2605.11151v3 Announce Type: replace
Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key ch...
By Andrew Choi, Wei Xu
arXiv:2601.13885v2 Announce Type: replace-cross
Abstract: Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluatio...
By Esma Balk{\i}r, Alice Pernthaller, Marco Basaldella, Jos\'e Hern\'andez-Orallo, Nigel Collier
Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e. g.