摘要:服务质量预测在服务计算领域中是一个热点研究问题.在历史QoS数据稀疏的情况下，设计一个满足用户个性化需求的服务质量预测方法成为一项挑战.为了解决这一挑战问题，提出一种基于SOM神经网络的服务质量预测方法SOMQP.首先，基于历史QoS数据，应用SOM神经网络算法分别对用户和服务进行聚类，得到用户关系矩阵和服务关系矩阵；进而，综合考虑用户信誉和服务关联性，采用一种新的Top-k选择机制获得相似用户和相似服务；最后，采用基于用户和基于项目的混合策略对缺失的QoS值进行预测.在真实的数据集WS-Dream上进行了大量实验，结果表明，与经典的CF算法和K-means算法相比，该方法在较大程度上提高了QoS的预测精度.



Abstract:Quality prediction for services is a hot research topic for service recommendation and composition. It's a challenge to design an accurate quality prediction approach to meet the user's personalized needs due to the sparsity of QoS historical data. In order to solve the challenging problem, this paper proposes a SOM neural network based service quality prediction approach (SOMQP). First, based on historical QoS data, the new approach clusters on users and services respectively by applying SOM neural network algorithm, and then a new top-k selection mechanism is adopted to obtain similar users and similar services based on the comprehensive consideration of user reputation and service relevance. Finally, a hybrid user-based and item-based strategy is used to predict the missing QoS value. A set of comprehensive experiments are conducted on the real Web service dataset WS-Dream, the results indicate that compared with the classical CF and K-means methods, the presented approach achieves higher QoS prediction accuracy.



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