
Denoising Implicit Feedback for Cold-start Recommendation
The researchers introduce DIF, a model-agnostic method to denoise implicit feedback in recommender systems, specifically targeting the cold-start problem. By leveraging content similarity and pseudo-label confidence, the model effectively corrects noisy labels for new items.






