A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research.Among the existing QoS prediction methods, the collaborative filtering(CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction(PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first use the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. The results indicate that PLRes model is effective for QoS prediction and at the density of 5%-30%, which means the data is sparse, it significantly outperforms a state-of-the-art approach LDCF by 12.35%-15.37% in terms of MAE.

QoS预测的概率分布和位置感知ResNet方法

近年来，在线服务的数量迅速增长，通过云平台调用所需的服务已成为主要趋势。如何在大量未使用的服务中帮助用户选择和推荐高质量的服务已成为研究的热点。.. 在现有的QoS预测方法中，协同过滤（CF）方法只能学习低维线性特征，其效果受到稀疏数据的限制。尽管现有的深度学习方法可以更好地捕获高维非线性特征，但是大多数方法仅使用身份的单一特征，并且网络加深梯度消失的问题严重，因此QoS预测的效果并不理想。为了解决这些问题，我们提出了一种用于QoS预测（PLRes）的高级概率分布和位置感知ResNet方法。这种方法考虑了用户和服务的历史调用概率分布以及位置特征，并且首先在Resc预测中使用ResNet来重用这些功能，减轻了梯度消失和模型退化的问题。在真实的Web服务数据集WS-DREAM上进行了一系列实验。结果表明，PLRes模型对于QoS预测是有效的，并且密度为5％-30％，这意味着数据稀疏，在性能方面，它明显优于最新方法LDCF 12.35％-15.37％ MAE。 （阅读更多）