Probabilistic Deep Supervision Network: A Noise-Resilient Approach for
QoS Prediction
- URL: http://arxiv.org/abs/2308.02580v1
- Date: Thu, 3 Aug 2023 16:13:46 GMT
- Title: Probabilistic Deep Supervision Network: A Noise-Resilient Approach for
QoS Prediction
- Authors: Ziliang Wang, Xiaohong Zhang, Sheng Huang, Wei Zhang, Dan Yang and
Meng Yan
- Abstract summary: Probabilistic Deep Supervision Network (PDS-Net) is a multitasking framework for prediction.
PDS-Net effectively reduces errors resulting from the propagation of corrupted data, leading to more accurate predictions.
Experimental evaluations on two real-world datasets demonstrate that the proposed PDS-Net outperforms state-of-the-art baselines.
- Score: 22.382138315436222
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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