Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem
Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem
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DOI：
10.1109/tsc.2018.2821686
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发表时间：
2021-05
影响因子：
8.1
通讯作者：
Duksan Ryu;Kwangkyu Lee;Jongmoon Baik
中科院分区：
文献类型：
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作者：
Duksan Ryu;Kwangkyu Lee;Jongmoon Baik
许多基于Web的软件系统都是以组合服务的形式开发的。准确地预测原子Web服务的服务质量（QoS）是非常重要的，因为这种组合服务的性能在很大程度上取决于所采用的原子Web服务的性能。近年来，基于协同过滤的Web服务QoS值预测方法已经被提出。然而，他们主要面临着一个冷启动问题，这是很难作出可靠的预测，由于高度稀疏的历史数据，新引入的用户和Web服务，现有的工作只处理的情况下，新引入的用户。在这篇文章中，我们提出了一个基于位置的矩阵分解使用偏好传播方法（LMF-PP）来解决冷启动问题。LMF-PP融合了调用相似度和邻域相似度，并利用融合后的相似度进行偏好传播。在真实的世界数据集上，将LMF-PP与现有的方法进行了比较。基于实验结果，LMF-PP表现出更好的性能比现有的方法在冷启动环境中，以及在热启动环境。
Many web-based software systems have been developed in the form of composite services. It is important to accurately predict the Quality of Service (QoS) value of atomic web services because the performance of such composite services depends greatly on the performance of the atomic web service adopted. In recent years, collaborative filtering based methods for predicting the web service QoS values have been proposed. However, they are mainly faced with a cold start problem that is difficult to make reliable prediction due to highly sparse historical data, newly introduced users and web services, and the existing work only deals with the case of newly introduced users. In this article, we propose a Location-based Matrix Factorization using a Preference Propagation method (LMF-PP) to address the cold start problem. LMF-PP fuses invocation and neighborhood similarity, and then the fused similarity is utilized by preference propagation. LMF-PP is compared with existing approaches on the real world dataset. Based on the experimental results, LMF-PP shows better performance than existing approaches in cold start environments as well as in warm start environments.