Web服务Qo S预测与主动推荐方法综述

QoS prediction and active recommendation for Web services overview

随着Web服务数量的迅猛增加,如何及时有效地根据服务质量(Quality of Service,Qo S)推荐优质服务已成为研究热点。在实际应用中,由于网络环境、地理位置和服务运行环境等存在差异,导致Qo S的不同并表现出强烈的不稳定性,仅通过挖掘Qo S数据中近邻信息难以保证预测的准确度。协同过滤算法作为现今在解决Qo S预测准确度方面的一个重要方法,就非常值得研究。本文针对基于Web服务Qo S预测的主动服务推荐方法及其存在的问题进行了分析和总结。总结了当下Qo S预测方法的研究趋势和发展前景。

With the rapid increase in the number of Web services,how to recommend quality services according to the quality of service(Quality of Service,QoS)in a timely and effective manner has become a hot research topic.In practical application,due to the difference of network environment,geographical location and service running environment,QoS is different,which shows strong instability.It is difficult to guarantee the accuracy of prediction only by mining neighbor information in QoS data.As an important method to solve the problem of QoS prediction accuracy,collaborative filtering algorithm is worth studying.In this paper,an active service recommendation method based on Web service QoS prediction and its existing problems are analyzed and explored.Subsequently,the research trend and development prospect of current QoS prediction methods are summarized.