DAR: Dimension-Adaptive Recommendation with Multi-Granular Noise Control

Implicit feedback has become the primary source of training data for modern recommender systems due to its abundance and ease of collection. However, the inherent noise in implicit feedback poses significant challenges to model training. Existing denoising approaches either completely remove suspected noisy interactions (re-sampling) or uniformly adjust their importance (re-weighting). Such coarse-grained treatments fail to capture the complex nature of noise in real-world scenarios, where different aspects of an interaction may have varying noise levels.