23 citations · 82 across the 29 of their papers we have counts for
29 papers
Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval
Olivier Jeunen
Approximate Nearest Neighbour search indices form the backbone of real-world recommender systems, enabling real-time candidate retrieval over million-item catalogues. Typically, a…
Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
Neeti Pokharna, Olivier Jeunen, Yatharth Saraf +1
Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings. These metrics are typically heavy-tailed…
Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
Marketing and product personalisation provide a prominent and visible use-case for the application of Information Retrieval methods across several business domains. Recently, agent…
Meta Off-Policy Estimation
Olivier Jeunen
Off-policy estimation (OPE) methods enable unbiased offline evaluation of recommender systems, directly estimating the online reward some target policy would have obtained, from of…
Agentic Personalisation of Cross-Channel Marketing Experiences
Sami Abboud, Eleanor Hanna, Olivier Jeunen +2
Consumer applications provide ample opportunities to surface and communicate various forms of content to users. From promotional campaigns for new features or subscriptions, to eve…
Procedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agents
Schaun Wheeler, Olivier Jeunen
Large Language Models (LLMs) represent a landmark achievement in Artificial Intelligence (AI), demonstrating unprecedented proficiency in procedural tasks such as text generation,…
Counterfactual Inference under Thompson Sampling
Olivier Jeunen
Recommender systems exemplify sequential decision-making under uncertainty, strategically deciding what content to serve to users, to optimise a range of potential objectives. To b…
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…
: Off-Policy Estimation with Pairs of Policies
Olivier Jeunen, Aleksei Ustimenko
The off-policy paradigm casts recommendation as a counterfactual decision-making task, allowing practitioners to unbiasedly estimate online metrics using offline data. This leads t…
Optimal Baseline Corrections for Off-Policy Contextual Bandits
Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis +1
The off-policy learning paradigm allows for recommender systems and general ranking applications to be framed as decision-making problems, where we aim to learn decision policies t…
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 Metrics that Maximise Power for Accelerated A/B-Tests
Olivier Jeunen, Aleksei Ustimenko
Online controlled experiments are a crucial tool to allow for confident decision-making in technology companies. A North Star metric is defined (such as long-term revenue or user r…
Variance Reduction in Ratio Metrics for Efficient Online Experiments
Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko +1
Online controlled experiments, such as A/B-tests, are commonly used by modern tech companies to enable continuous system improvements. Despite their paramount importance, A/B-tests…
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…
Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation
Srijan Saket, Olivier Jeunen, Md. Danish Kalim
Emerging short-video platforms like TikTok, Instagram Reels, and ShareChat present unique challenges for recommender systems, primarily originating from a continuous stream of new…
Offline Recommender System Evaluation under Unobserved Confounding
Olivier Jeunen, Ben London
Off-Policy Estimation (OPE) methods allow us to learn and evaluate decision-making policies from logged data. This makes them an attractive choice for the offline evaluation of rec…
Ad-load Balancing via Off-policy Learning in a Content Marketplace
Hitesh Sagtani, Madan Jhawar, Rishabh Mehrotra +1
Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and re…
A Probabilistic Position Bias Model for Short-Video Recommendation Feeds
Olivier Jeunen
Modern web-based platforms show ranked lists of recommendations to users, attempting to maximise user satisfaction or business metrics. Typically, the goal of such systems boils do…
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top- Recommendation
Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko
Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluatio…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
Gabriel Bénédict, Olivier Jeunen, Samuele Papa +3
In this paper we propose RecFusion, which comprise a set of diffusion models for recommendation. Unlike image data which contain spatial correlations, a user-item interaction matri…
A Common Misassumption in Online Experiments with Machine Learning Models
Olivier Jeunen
Online experiments such as Randomised Controlled Trials (RCTs) or A/B-tests are the bread and butter of modern platforms on the web. They are conducted continuously to allow platfo…
Practical Bandits: An Industry Perspective
Bram van den Akker, Olivier Jeunen, Ying Li +3
The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a…