3 papers
cs.IR2024
Large Language Models as Recommender Systems: A Study of Popularity Bias
Jan Malte Lichtenberg, Alexander Buchholz, Pola Schwöbel
The issue of popularity bias -- where popular items are disproportionately recommended, overshadowing less popular but potentially relevant items -- remains a significant challenge…
cs.IR2023
Unbiased Offline Evaluation for Learning to Rank with Business Rules
Matej Jakimov, Alexander Buchholz, Yannik Stein +1
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 requ…
cs.LG2023
Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation
Jan Malte Lichtenberg, Alexander Buchholz, Giuseppe Di Benedetto +2
"Clipping" (a.k.a. importance weight truncation) is a widely used variance-reduction technique for counterfactual off-policy estimators. Like other variance-reduction techniques, c…