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Preprint Aug 2026

CRAMER: Control via Request-Aware Masking for Editing Recommenders

Control via Request-Aware Masking for Editing Recommenders (CRAMER), a framework that takes users' natural-language requests to immediately change sequential recommendation models' behavior, establishing a new paradigm for request-aware sequential recommendation.

Zhiyuan Su, Naihe Feng, Zhen Qin et al. · 0 citations
Book Open access Jul 2026

Exposure-Based Reinforcement Learning to Rank

This work considerably improves RL for LTR methodology by increasing its effectiveness, efficiency, and ease of application, and proposes an abstraction that places gradient estimation behind a document-exposure distribution that enables seamless plug-and-play integration with auto-differentiation.

Harrie Oosterhuis, R. Jagerman, Zhen Qin et al. · 0 citations

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