SAHC-NS：面向隐式协同过滤的结构感知与难度校准负采样方法
SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering
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中文总结 AI 辅助
本文针对隐式协同过滤负采样忽略跨层结构差异与候选池难度的问题，提出SAHC-NS方法，通过分层匹配分数与难度校准模块提升负采样效果，实验验证其优于现有方法。
中文摘要 AI 辅助
负采样是隐式协同过滤（CF）的关键组成部分，它能让推荐系统有效学习用户偏好。现有负采样方法大多遵循两阶段范式：先为每个用户构建候选负样本池，再根据预定义的采样规则从池中选取负样本。然而，这些方法通常忽略了不同用户候选负样本池的难度差异，难以根据候选池条件自适应调整负样本的难度与信息量。此外，大多数现有采样器主要通过用户和物品最终聚合嵌入计算的匹配分数来评估候选负样本，忽略了多跳邻域聚合所捕获的结构差异，导致负样本的训练价值可能未被充分表征。为解决这些问题，本文提出SAHC-NS，一种结构感知与难度校准负采样方法。具体而言，SAHC-NS利用分层匹配分数的均值和标准差分别捕获候选负样本的整体匹配强度和跨层结构差异，使其能结合跨层结构差异选取有信息量的负样本，而非仅依赖最终匹配分数；此外，SAHC-NS引入候选池感知的难度校准模块，根据候选负样本池的难度动态调整负样本增强强度，生成难度可控的负样本。大量实验表明，SAHC-NS优于现有负采样方法。
英文摘要
Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods mostly follow a two-stage paradigm: they first construct a candidate negative pool for each user and then select negative samples from the pool according to predefined sampling rules. However, these methods usually overlook the hardness variation of candidate negative pools across users, making it difficult to adaptively adjust the hardness and informativeness of negative samples according to candidate-pool conditions. In addition, most existing samplers evaluate candidate negatives mainly through a matching score computed from the final aggregated user and item embeddings, while ignoring the structural differences captured by multi-hop neighborhood aggregation. As a result, the training value of negatives may be insufficiently characterized. To address these issues, we propose SAHC-NS, a Structure-Aware and Hardness-Calibrated Negative Sampling method. Specifically, SAHC-NS uses the mean and standard deviation of layer-wise matching scores to capture the overall matching strength and cross-layer structural discrepancy of candidate negatives, respectively. This enables SAHC-NS to select informative negatives by taking cross-layer structural discrepancy into account, rather than relying solely on final matching scores. Moreover, SAHC-NS introduces a candidate-pool-aware hardness calibration module to dynamically adjust negative augmentation strength according to candidate-pool hardness, producing hardness-controllable negatives. Extensive experiments demonstrate the superiority of SAHC-NS over existing negative sampling methods.
