一种概率分布和位置感知的QoS预测方法
A Probability Distribution and Location-aware ResNet Approach for QoS Prediction
摘要：
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。如何帮助用户在大量未使用的服务中选择和推荐高质量的服务成为研究的热点问题。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。现有的深度学习方法虽然能较好地捕捉高维非线性特征，但大多数只使用身份这一单一特征，网络深化梯度消失问题严重，QoS预测效果不理想。为了解决这些问题，我们提出了一个先进的概率分布和位置感知的ResNet方法的QoS预测（PLRes）。该方法综合考虑了用户和服务的历史调用概率分布和位置特征，并在QoS预测中首次使用ResNet对特征进行重用，避免了梯度消失和模型退化问题。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模型是一种有效的QoS预测方法，在数据稀疏的密度为5%~30%时，PLRes模型的MAE比LDCF模型高出12.35%~15.37%.
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
