12 citations · 23 across the 3 of their papers we have counts for
3 papers
cs.IR2025★ 1 cited
Learning to Rank with Variable Result Presentation Lengths
Norman Knyazev, Harrie Oosterhuis
Learning to Rank (LTR) methods generally assume that each document in a top-K ranking is presented in an equal format. However, previous work has shown that users' perceptions of r…
cs.IR2023★ 10 cited
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions
Norman Knyazev, Harrie Oosterhuis
Most Recommender Systems (RecSys) do not provide an indication of confidence in their decisions. Therefore, they do not distinguish between recommendations of which they are certai…
cs.IR2022★ 12 cited
The Bandwagon Effect: Not Just Another Bias
Norman Knyazev, Harrie Oosterhuis
Optimizing recommender systems based on user interaction data is mainly seen as a problem of dealing with selection bias, where most existing work assumes that interactions from di…