Reliable Hard Negative Sampling for Implicit Collaborative Filtering

Implicit collaborative filtering (CF) models are widely used in recommender systems due to their cost-effective data collection and broad applicability. Negative sampling methods play a crucial role in training implicit CF models. However, most of these methods ignore the false negative problem, and the remaining methods rely solely on statistical features to identify them. The integration of contextual information to address this issue remains unexplored. To fill this gap, we propose a novel sampling method called Reliable Hard Negative Sampling (RHNS). Our approach consists of two key components: a hard negative sampling module, which constructs hard negative samples through a controllable dimension-level fusion approach, and a reliable transformation module, which incorporates contextual information to transform hard negative samples, including those that may be false negatives, into more reliable hard negative samples. We provide theoretical insights showing that the hard negative sampling module tends to generate harder negative samples than existing hard negative sampling methods, while the reliable transformation module tends to mitigate false negative risk. Extensive experimental results on five real-world datasets demonstrate the superiority of RHNS.