13 citations · 20 across the 4 of their papers we have counts for
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cs.LG2022
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
Alexander Buchholz, Ben London, Giuseppe di Benedetto +1
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. Unfortunately, widely used off-p…
stat.ML2022★ 1 cited
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…