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Found 7 papers, 0 papers with code
Unbiased Offline Evaluation for Learning to Rank with Business Rules
For industrial learning-to-rank (LTR) systems, it is common that the output of a ranking model is modified, either as a results of post-processing logic that enforces business requirements, or as a result of unforeseen design flaws or bugs present in real-world production systems.
Fair Effect Attribution in Parallel Online Experiments
We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values.
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production.
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
When adopting the PL model as a ranking policy, both tasks require the computation of expectations with respect to the model.
Ranker-agnostic Contextual Position Bias Estimation
This paper introduces a method for modeling the probability of an item being seen in different contexts, e. g., for different users, with a single estimator.
Distributed Computation for Marginal Likelihood based Model Choice
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets.
Quasi-Monte Carlo Variational Inference
We also propose a new algorithm for Monte Carlo objectives, where we operate with a constant learning rate and increase the number of QMC samples per iteration.
