1 citations · 1 across the 3 of their papers we have counts for
4 papers
Fair Effect Attribution in Parallel Online Experiments
Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto +3
A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous exp…
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto +3
The Plackett-Luce (PL) model is ubiquitous in learning-to-rank (LTR) because it provides a useful and intuitive probabilistic model for sampling ranked lists. Counterfactual offlin…
Ranker-agnostic Contextual Position Bias Estimation
Oriol Barbany Mayor, Vito Bellini, Alexander Buchholz +4
Learning-to-rank (LTR) algorithms are ubiquitous and necessary to explore the extensive catalogs of media providers. To avoid the user examining all the results, its preferences ar…
Learning to Rank in the Position Based Model with Bandit Feedback
Beyza Ermis, Patrick Ernst, Yannik Stein +1
Personalization is a crucial aspect of many online experiences. In particular, content ranking is often a key component in delivering sophisticated personalization results. Commonl…