FairNS: Fair Negative Sampling in Collaborative Filtering via Diffusion Models

Collaborative filtering (CF) methods commonly use negative sampling to improve preference learning by contrasting observed interactions with unobserved items. While effective, conventional practice implicitly treats all unclicked items as equally informative, regardless of their semantic group (e.g., genre or category). This overlooks a critical limitation: models may exploit coarse group distinctions rather than genuine fine-grained preferences, thereby inducing exposure imbalances across item groups. Such imbalances constitute a violation of item-side fairness, which seeks equitable exposure and evaluation for items from different semantic groups. When negative samples are drawn predominantly from groups semantically distant from a user’s positives, the learning signal becomes biased and comparisons unfair. We therefore revisit negative sampling through the lens of item-side fairness and argue that genuine fairness requires context-aware sampling that ensures like-for-like comparisons within each semantic group. To this end, we introduce FairNS, a diffusion-based sampling framework that generates negative samples within the same semantic group as the user’s positives, encouraging fair intra-group contrasts that respect group integrity. By centering training on these intra-group comparisons, FairNS mitigates cross-group bias and enables the recommender to learn more precise user preferences. FairNS is optimized via a bi-level objective that jointly refines the sampling mechanism and the recommendation model. Experiments on three benchmark datasets show that FairNS achieves a favorable fairness–accuracy trade-off.