Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction. However, existing QoS prediction techniques may perform poorly in the presence of noise data, such as fake location information or virtual gateways. In this paper, we propose the Probabilistic Deep Supervision Network (PDS-Net), a novel framework for QoS prediction that addresses this issue. PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate layers and learns probability spaces for both known features and true labels. Moreover, PDS-Net employs a condition-based multitasking loss function to identify objects with noise data and applies supervision directly to deep features sampled from the probability space by optimizing the Kullback-Leibler distance between the probability space of these objects and the real-label probability space. Thus, PDS-Net effectively reduces errors resulting from the propagation of corrupted data, leading to more accurate QoS predictions. Experimental evaluations on two real-world QoS datasets demonstrate that the proposed PDS-Net outperforms state-of-the-art baselines, validating the effectiveness of our approach.

翻译：服务质量（QoS）预测是推荐系统中的关键任务，准确预测未知QoS值能够提升用户满意度。然而，现有QoS预测技术在面对噪声数据（如虚假位置信息或虚拟网关）时可能表现不佳。本文提出概率深度监督网络（PDS-Net），一种用于解决该问题的新型QoS预测框架。PDS-Net利用基于高斯分布的概率空间对中间层进行监督，并同时学习已知特征与真实标签的概率空间。此外，PDS-Net采用基于条件的多任务损失函数识别包含噪声数据的对象，通过优化这些对象概率空间与真实标签概率空间之间的Kullback-Leibler距离，直接对从概率空间中采样的深层特征进行监督。因此，PDS-Net有效减少了因污染数据传播导致的误差，从而实现了更精确的QoS预测。在两个真实QoS数据集上的实验结果表明，所提出的PDS-Net优于当前最优基线方法，验证了本方法的有效性。