Personalised QoS-based web service recommendation with service neighbourhood-enhanced matrix factorisation
Personalised QoS-based web service recommendation with service neighbourhood-enhanced matrix factorisation
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DOI：
10.1504/ijwgs.2015.067156
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Jianwei Yin;Yueshen Xu
中科院分区：
文献类型：
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作者：
Jianwei Yin;Yueshen Xu
随着服务在云计算和企业信息系统中的广泛应用，Web服务推荐已经成为一项紧迫的任务，而服务质量(Quality of Service，QOS)预测起着至关重要的作用。本文基于概率矩阵分解模型，提出了三种服务邻域增强预测模型。在第一个模型中，在识别出共享相似服务质量记录的邻居之后，我们利用活动服务及其邻居的特征向量来学习预测值。然后，在第二个模型中，通过结合根据相似度计算的每个邻居的权重来增强学习过程。然后，在最终的模型中，我们强调了同一家公司所拥有的服务与主动服务的特殊作用，并最小化了它们之间的特征向量差异。此外，我们还提出了一个统一的推荐框架，涉及到这三个模型。最后，我们进行了大量的实验，证明了我们的模型的有效性。
Web service recommendation has been an urgent task with the wide adoption of services in cloud computing and enterprise information systems, and Quality of Service (QoS) prediction takes a critical role. In this paper, based on probabilistic matrix factorisation model, we propose three service-neighbourhood enhanced prediction models. In the first model, after identifying the neighbours sharing similar QoS records, we learn the predicted value both utilising feature vectors of the active service and its neighbours. Then, in the second model, the learning process is enhanced through the integration with each neighbour's weight computed from the similarity. Afterwards, in the final model, we emphasise the special roles of services possessed by the same company with the active service, with minimising the difference of feature vectors between them. Further, we propose a unified recommendation framework, involving in the three models. Finally, we conduct extensive experiments showing our models' effectiveness.