EDUCATION & TRAINING
Debiasing Graph Neural Networks for Recommendation with Causal RL
Dev.to Machine Learning
About This Tutorial
As part of my undergraduate research in Graph Neural Networks (GNNs) and Causal Inference, I've been exploring a major flaw in modern recommender systems: observational bias. Standard recommendation algorithms - even state-of-the-art GNNs like LightGCN and NGCF - learn from biased data. Popular items get shown often, which leads to clicks, creating a feedback loop that reinforces popularity bias and buries niche items. To solve this, I built an open-source framework that combines GNNs with Causal Reinforcement Learning to debias recommendations. Here is how I approached it.