StabCF: A Stabilized Training Method for Collaborative Filtering

Collaborative Filtering (CF) with implicit feedback is widely used in personalized recommender systems. In most real-world scenarios, only implicit feedback signals are available, making CF training heavily dependent on sampling-based paradigms - typically optimized via pairwise ranking losses such as Bayesian Personalized Ranking (BPR). This simple yet effective approach has achieved remarkable success and remains the foundation of many modern recommender models. However, despite its empirical success, little attention has been paid to the inherent training instability issue under this sampling-based paradigm. In this paper, we conduct an in-depth analysis of training stability and find that unstable training not only hinders convergence but also leads to fluctuating and suboptimal recommendation performance. We identify two fundamental sources of this instability in CF: (1) noisy or sparse positive samples, where a single observed interaction may not reliably reflect user preference; and (2) inconsistent negative samples, where randomly drawn negatives from unobserved space vary drastically in negative hardness, leading to uninformative or noisy gradient updates. To address these two challenges, we propose StabCF, a Stabilized Training Method for Collaborative Filtering, which improves training stability by synthesizing enriched positive samples from historical positives and constructing consistent hard negatives through user-aware negatives mixing. By replacing raw training triplets with synthesized positive-negative pairs, StabCF effectively smooths the training dynamics and improves convergence stability. Extensive experiments on three public datasets demonstrate that StabCF not only significantly stabilizes the training process but also achieves superior recommendation performance. Our PyTorch implementation is available at https://github.com/Wu-Xi/StabCF.