Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering
Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering
复制标题
通过基于邻域的协同过滤来预测选择的服务质量
DOI：
10.1109/tsmca.2012.2210409
复制
发表时间：
2013-03-01
影响因子：
8.7
通讯作者：
Wu, Zhaohui
中科院分区：
文献类型：
--
作者：
Wu, Jian;Chen, Liang;Wu, Zhaohui
基于服务质量的服务选择是面向服务计算的一个重要问题。以往研究的一个共同前提是，服务的QoS值的目标用户应该是所有已知的。然而，许多QoS值在现实中是未知的。本文提出了一种基于邻域的协同过滤方法来预测这样的未知值的QoS为基础的选择。与现有方法相比，该方法具有3个新的特点：1）基于调整余弦的相似度计算，消除了不同QoS尺度的影响; 2）数据平滑处理，提高了预测精度; 3）相似度融合方法，处理数据稀疏问题。此外，提出了一种两阶段邻居选择策略，以提高其可扩展性。一项基于公共数据集的广泛性能研究证明了其有效性。
Quality-of-service-based (QoS) service selection is an important issue of service-oriented computing. A common premise of previous research is that the QoS values of services to target users are supposed to be all known. However, many of QoS values are unknown in reality. This paper presents a neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection. Compared with existing methods, the proposed method has three new features: 1) the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; 2) a data smoothing process to improve prediction accuracy; and 3) a similarity fusion approach to handle the data sparsity problem. In addition, a two-phase neighbor selection strategy is proposed to improve its scalability. An extensive performance study based on a public data set demonstrates its effectiveness.