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Found 6 papers, 3 papers with code
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
Our experiments reveal that gains in click prediction do not necessarily translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Motivated by the efficiency of $L_2$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.
An Offline Metric for the Debiasedness of Click Models
We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.
Generative Slate Recommendation with Reinforcement Learning
Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives
In this paper, we argue that the paradigm commonly adopted for offline evaluation of sequential recommender systems is unsuitable for evaluating reinforcement learning-based recommenders.
