Learning from implicit feedback is a fundamental problem in modern recommender systems, where only positive interactions are observed and explicit negative signals are unavailable. In such settings, negative sampling plays a critical role in model training by constructing negative items that enable effective preference learning and ranking optimization. However, designing reliable negative sampling strategies remains challenging, as they must simultaneously ensure realness, hardness, and interpretability. To this end, we propose \textbf{ICPNS (In-Community Popularity Negative Sampling)}, a novel framework that leverages user community structure to identify reliable and informative negative samples. Our approach is grounded in the insight that item exposure is driven by latent user communities. By identifying these communities and utilizing in-community popularity, ICPNS effectively approximates the probability of item exposure. Consequently, items that are popular within a user's community but remain unclicked are identified as more reliable true negatives. Extensive experiments on four benchmark datasets demonstrate that ICPNS yields consistent improvements on graph-based recommenders and competitive performance on MF-based models, outperforming representative negative sampling strategies under a unified evaluation protocol.

翻译：从隐式反馈中学习是现代推荐系统的一个基本问题，其中仅能观察到正向交互而无法获得显式的负向信号。在此类场景下，负采样通过构建负样本项以实现有效的偏好学习和排序优化，在模型训练中起着关键作用。然而，设计可靠的负采样策略仍然具有挑战性，因为它们必须同时确保真实性、难度和可解释性。为此，我们提出了 \textbf{ICPNS（基于社区内流行度的负采样）}，这是一种利用用户社区结构来识别可靠且信息丰富的负样本的新颖框架。我们的方法基于以下洞见：物品曝光是由潜在的用户社区驱动的。通过识别这些社区并利用社区内流行度，ICPNS 有效地近似了物品曝光的概率。因此，在用户所属社区内流行但未被点击的物品被识别为更可靠的真正负样本。在四个基准数据集上的大量实验表明，ICPNS 在图神经网络推荐器上带来了持续的改进，并在基于矩阵分解的模型上取得了有竞争力的性能，在统一的评估协议下优于代表性的负采样策略。