research
∙
09/19/2023
Ad-load Balancing via Off-policy Learning in a Content Marketplace
Ad-load balancing is a critical challenge in online advertising systems,...
share
research
∙
09/08/2023
Offline Recommender System Evaluation under Unobserved Confounding
Off-Policy Estimation (OPE) methods allow us to learn and evaluate decis...
research
∙
07/27/2023
On (Normalised) Discounted Cumulative Gain as an Offline Evaluation Metric for Top-n Recommendation
Approaches to recommendation are typically evaluated in one of two ways:...
research
∙
07/26/2023
A Probabilistic Position Bias Model for Short-Video Recommendation Feeds
Modern web-based platforms show ranked lists of recommendations to users...
research
∙
06/15/2023
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
In this paper we propose RecFusion, which comprise a set of diffusion mo...
research
∙
04/21/2023
A Common Misassumption in Online Experiments with Machine Learning Models
Online experiments such as Randomised Controlled Trials (RCTs) or A/B-te...
research
∙
02/02/2023
Practical Bandits: An Industry Perspective
The bandit paradigm provides a unified modeling framework for problems t...
research
∙
10/11/2022
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
The ability to answer causal questions is crucial in many domains, as ca...
research
∙
09/18/2022
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation
Both in academic and industry-based research, online evaluation methods ...
research
∙
09/18/2019
Learning from Bandit Feedback: An Overview of the State-of-the-art
In machine learning we often try to optimise a decision rule that would ...
research
∙
07/26/2019