Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS). Despite this regional network similarity, many existing techniques overlook its potential, resulting in subpar performance arising from challenges such as data sparsity and label imbalance. In this paper, we introduce the regional-based dual latent state learning network(R2SL), a novel deep learning framework designed to overcome the pitfalls of traditional individual object-based prediction techniques in Quality of Service (QoS) prediction. Unlike its predecessors, R2SL captures the nuances of regional network behavior by deriving two distinct regional network latent states: the city-network latent state and the AS-network latent state. These states are constructed utilizing aggregated data from common regions rather than individual object data. Furthermore, R2SL adopts an enhanced Huber loss function that adjusts its linear loss component, providing a remedy for prevalent label imbalance issues. To cap off the prediction process, a multi-scale perception network is leveraged to interpret the integrated feature map, a fusion of regional network latent features and other pertinent information, ultimately accomplishing the QoS prediction. Through rigorous testing on real-world QoS datasets, R2SL demonstrates superior performance compared to prevailing state-of-the-art methods. Our R2SL approach ushers in an innovative avenue for precise QoS predictions by fully harnessing the regional network similarities inherent in objects.

翻译：特定区域内的个体对象（无论是用户还是服务）往往表现出相似的网络状态，这是由于它们源自同一城市或自治系统（AS）。尽管存在这种区域网络相似性，许多现有技术却忽视了其潜力，导致因数据稀疏性和标签不平衡等挑战而产生的性能不佳。本文提出基于区域的双潜在状态学习网络（R2SL），这是一种新颖的深度学习框架，旨在克服传统基于个体对象的服务质量（QoS）预测技术的缺陷。与先前方法不同，R2SL通过推导两个不同的区域网络潜在状态——城市网络潜在状态和AS网络潜在状态——来捕捉区域网络行为的细微差别。这些状态是利用来自共同区域的聚合数据而非个体对象数据构建的。此外，R2SL采用改进的Huber损失函数，调整其线性损失分量，为解决普遍的标签不平衡问题提供了方案。在预测过程的最后，利用多尺度感知网络来解释集成特征图——该图融合了区域网络潜在特征及其他相关信息——最终完成QoS预测。通过对真实世界QoS数据集的严格测试，R2SL展现出优于当前主流方法的性能。我们的R2SL方法通过充分利用对象固有的区域网络相似性，为精确的QoS预测开辟了一条创新途径。