Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision
This addresses feature noise in QoS prediction for web recommendation systems, which is an incremental improvement over existing methods that focus on label noise.
The paper tackles the problem of feature noise in Quality of Service (QoS) prediction for web recommendation systems by proposing the Probabilistic Deep Supervision Network (PDS-Net), which improves prediction accuracy by identifying and mitigating noisy features. The results show average MAE improvements of 8.91% and 8.32% on two real-world datasets compared to state-of-the-art models.
Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on Dataset D1 and 8.32% on Dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments.