Debiasing Graph Neural Networks for Recommendation with Causal RL
The article discusses the issue of observational bias in modern recommender systems, particularly in Graph Neural Networks (GNNs). The author, Tasfin Mahmud, presents an open-source framework that integrates GNNs with Causal Reinforcement Learning to address this bias. By employing various debiasing techniques, the framework aims to improve recommendation accuracy by focusing on true user preferences rather than popularity metrics.
- ▪Observational bias in recommendation systems leads to a feedback loop that favors popular items over niche ones.
- ▪The author developed a framework that combines GNNs with Causal Reinforcement Learning to mitigate this bias.
- ▪Four debiasing techniques were implemented, including Inverse Propensity Scoring and Causal Policy Gradient.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3946759) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Tasfin Mahmud Posted on May 23 • Originally published at tasfinmahmud.github.io Debiasing Graph Neural Networks for Recommendation with Causal RL #machinelearning #python #opensource #gnn 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.
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