Most implicit collaborative filtering (CF) models are trained with negative sampling, where existing work designs sophisticated strategies for high-quality negatives while largely overlooking the exploration of positive samples. Although some denoising recommendation methods can be applied to implicit CF for denoising positive samples, they often sparsify positive supervision. Moreover, these approaches generally overlook user activity bias during training, leading to insufficient learning for inactive users. To address these issues, we propose a simple yet effective negative sampling plugin, PSP-NS, from the perspective of enhancing positive supervision signals. It builds a user-item bipartite graph with edge weights indicating interaction confidence inferred from global and local patterns, generates positive sample pairs via replication-based reweighting to strengthen positive signals, and adopts an activity-aware weighting scheme to effectively learn inactive users' preferences. We provide theoretical insights from a margin-improvement perspective, explaining why PSP-NS tends to improve ranking quality (e.g., Precision@k/Recall@k), and conduct extensive experiments on four real-world datasets to demonstrate its superiority. For instance, PSP-NS boosts Recall@30 and Precision@30 by 32.11% and 22.90% on Yelp over the strongest baselines. PSP-NS can be integrated with various implicit CF recommenders or negative sampling methods to enhance their performance.

翻译：大多数隐式协同过滤（CF）模型采用负采样进行训练，现有工作设计了复杂的策略来获取高质量负样本，却很大程度上忽视了对正样本的探索。尽管一些去噪推荐方法可应用于隐式CF以对正样本去噪，但它们往往会稀疏化正监督信号。此外，这些方法通常在训练过程中忽略用户活跃度偏差，导致对非活跃用户的学习不足。为解决这些问题，我们从增强正监督信号的角度出发，提出了一种简单而有效的负采样插件PSP-NS。它构建了一个用户-物品二部图，其中边权重表示从全局和局部模式推断出的交互置信度；通过基于复制的重加权生成正样本对以强化正信号；并采用活动感知加权方案来有效学习非活跃用户的偏好。我们从边际改进的角度提供了理论分析，解释了为何PSP-NS倾向于提升排序质量（如Precision@k/Recall@k），并在四个真实世界数据集上进行了广泛实验以证明其优越性。例如，在Yelp数据集上，PSP-NS相较于最强基线将Recall@30和Precision@30分别提升了32.11%和22.90%。PSP-NS可与多种隐式CF推荐器或负采样方法集成以提升其性能。