4 citations · 5 across the 4 of their papers we have counts for
5 papers
Aligning Recommendations with User Popularity Preferences
Mona Schirmer, Anton Thielmann, Pola Schwöbel +4
Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and…
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…
A Linear Bandit for Seasonal Environments
Giuseppe Di Benedetto, Vito Bellini, Giovanni Zappella
Contextual bandit algorithms are extremely popular and widely used in recommendation systems to provide online personalised recommendations. A recurrent assumption is the stationar…
Non-exchangeable random partition models for microclustering
Giuseppe Di Benedetto, François Caron, Yee Whye Teh
Many popular random partition models, such as the Chinese restaurant process and its two-parameter extension, fall in the class of exchangeable random partitions, and have found wi…