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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
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Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models

机译：通过聚合非负潜在因子模型生成丢失QoS数据的高精度预测

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摘要

Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the fulfillment of nonnegativity constraints can better represent the positive value nature of QoS data, thereby boosting the prediction accuracy and 2) since QoS prediction is a learning task, it is promising to further improve the prediction accuracy with a carefully designed ensemble model. To achieve this, we first implement an NLF model for QoS prediction. This model is then diversified through feature sampling and randomness injection to form a diversified NLF model, based on which an ensemble is built. Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.
机译：自动Web服务选择是服务计算领域中的重要研究主题。在此过程中，基于历史服务调用的可靠的服务质量（QoS）预测对用户至关重要。这项工作旨在通过构建非负潜在因子（NLF）模型的集成，对丢失的QoS数据进行高度准确的预测。其动机是：1）满足非负约束条件可以更好地表示QoS数据的正值性质，从而提高预测精度； 2）由于QoS预测是一项学习任务，因此有希望通过谨慎地进一步提高预测精度设计的集成模型。为了实现这一点，我们首先实现了用于QoS预测的NLF模型。然后，通过特征采样和随机注入使该模型多样化，以形成一个多样化的NLF模型，并以此为基础构建一个整体。在两个大型的真实数据集上，所提出的集合与几种广泛使用的和最新的QoS预测器之间的比较结果表明，在预测准确度方面，前者的性能优于后者。

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