23 citations · 38 across the 7 of their papers we have counts for
7 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…
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…
Knowledge-aware Autoencoders for Explainable Recommender Sytems
Vito Bellini, Angelo Schiavone, Tommaso Di Noia +2
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem. After several years of research and ind…
Computing recommendations via a Knowledge Graph-aware Autoencoder
Vito Bellini, Angelo Schiavone, Tommaso Di Noia +2
In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields. Am…