Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrix

arXiv:2605.20721 · cs.LG · Submitted 2026-05-20 · Read on arXiv

cs.LG

Submitted: 2026-05-20

Updated: 2026-09-26

Terminology

• Accuracy — 11× in this paper · explained in 1 episode
• Robustness — 10× in this paper · explained in 5 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Distillation — 6× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Gaussian Mixture Model — 2× in this paper
• Gaussian Mixture Model (GMM) — 2× in this paper · explained in 2 episodes
• Meta-learning — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Under the Instance-Dependent Noise — 2× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Understanding the Interaction of Adversarial Training with Noisy Labels

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