基于社区发现的Web服务QoS预测

doi:10.19678/j.issn.1000-3428.0049479

摘要/Abstract

摘要：

传统的协同过滤方法预测未知服务质量(QoS)值时多数面临数据稀疏、冷启动和噪声影响等问题。为此,提出一种新的基于社区发现的QoS预测方法。通过谱聚类对用户进行社区划分,根据位置信息对Web服务聚类,并利用改进的混合协同过滤方法预测QoS值。实验结果表明,该方法可够缓解新用户的冷启动问题,与基于协同过滤的QoS预测方法相比,具有更高预测准确度。

关键词: Web服务, 服务质量预测, 社区发现, 谱聚类, 协同过滤, K-means聚类

Abstract:

Traditional collaborative filtering methods face many problems such as data sparsity,cold start and noise influences when predicting unknown Quality of Service(QoS) values.Therefore a new QoS prediction method based on community discovery is proposed.Users are divided into communities by spectral clustering,Web services are clustered according to their location information,and the improved hybrid collaborative filtering method is used to predict the QoS value.Experimental results show that this method can alleviate the cold start problem of new users,and it has higher prediction accuracy compared with the QoS prediction method based on collaborative filtering.

Key words: Web service, Quality of Service(QoS) prediction, community detection, spectral clustering, collaborative filtering, K-means clustering

中图分类号:

TP391

陆贝妮,杜育根. 基于社区发现的Web服务QoS预测[J]. 计算机工程, 2019, 45(3): 117-124.

LU Beini,DU Yugen. QoS Prediction of Web Service Based on Community Detection[J]. Computer Engineering, 2019, 45(3): 117-124.

https://www.ecice06.com/CN/Y2019/V45/I3/117

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