4 citations · 4 across the 12 of their papers we have counts for
4 papers · 1 filter
Powerful A/B-Testing Metrics and Where to Find Them
Olivier Jeunen, Shubham Baweja, Neeti Pokharna +1
Online controlled experiments, colloquially known as A/B-tests, are the bread and butter of real-world recommender system evaluation. Typically, end-users are randomly assigned som…
Multi-Objective Recommendation via Multivariate Policy Learning
Olivier Jeunen, Jatin Mandav, Ivan Potapov +4
Real-world recommender systems often need to balance multiple objectives when deciding which recommendations to present to users. These include behavioural signals (e.g. clicks, sh…
Learning-to-Rank with Nested Feedback
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in the Learning-to-Ran…
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
Olivier Jeunen, Hitesh Sagtani, Himanshu Doi +7
Practitioners who wish to build real-world applications that rely on ranking models, need to decide which modelling paradigm to follow. This is not an easy choice to make, as the r…