65 citations · 224 across the 12 of their papers we have counts for
16 papers
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya +2
Methods for reinforcement learning for recommendation (RL4Rec) are increasingly receiving attention as they can quickly adapt to user feedback. A typical RL4Rec framework consists…
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity
Harrie Oosterhuis
Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computation…
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness
Harrie Oosterhuis
Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a robust choice for optimizing relevance and fairness metrics. Unlike their deterministic counterparts that…
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank
Harrie Oosterhuis, Maarten de Rijke
Existing work in counterfactual Learning to Rank (LTR) has focussed on optimizing feature-based models that predict the optimal ranking based on document features. LTR methods base…
Learning from User Interactions with Rankings: A Unification of the Field
Harrie Oosterhuis
Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and pr…
Unifying Online and Counterfactual Learning to Rank
Harrie Oosterhuis, Maarten de Rijke
Optimizing ranking systems based on user interactions is a well-studied problem. State-of-the-art methods for optimizing ranking systems based on user interactions are divided into…