arXiv:2605. 20721v2 Announce Type: replace Abstract: Label noise is a central challenge in learning from implicit feedback for recommendation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.
arXiv:2606. 19658v1 Announce Type: new Abstract: Implicit feedback is widely used in recommender systems due to its accessibility and generality, yet it usually presents noisy samples (e.
arXiv:2506. 16114v3 Announce Type: replace-cross Abstract: Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios.
arXiv:2609.39829v1 Announce Type: new Abstract: Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning...
arXiv:2606. 03091v1 Announce Type: cross Abstract: Sequential recommendation systems are widely adopted but often deployed as black-box APIs, which has driven recent interest in model extraction to replicate their capabilities locally.
arXiv:2608.21243v1 Announce Type: cross Abstract: Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combi...