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中文摘要：
随着互联网以及Web服务技术的快速发展,相同功能的Web服务数量越来越多.在构建面向服务的应用时,服务质量（Qo S）作为Web服务的非功能特性开始被越来越多的用户所重视.为了向用户推荐高质量的服务,首先我们需要对服务质量进行预测.现今有很多关于Web服务Qo S预测的工作,这些研究大都关注在建模方法的优化上,忽视了辅助特征对于Qo S预测的影响.着重分析辅助特征对于Qo S预测的影响,例如服务类别和用户地理位置.为了实现此目标,基于因子分解机（Factorization Machines）设计并构建了一个统一的Qo S预测架构,该架构可以灵活、方便地考虑进多个辅助特征.结合服务类别和用户地理位置这两类辅助特征,提出了一种Qo S预测方法,并通过在真实数据上的实验证明了我们的方法的优越性.
英文摘要：
With the rapid growth of Internet and Web service technology, the number of Web services having the same function is getting more and more. When constructing service-oriented applications, Quality of Service（Qo S） as the non-functional properties of services is attracting more and more attention from users. To recommend high quality services to users, first we need to predict the quality of services. Now there are many research works on Web service Qo S prediction, but most of these works focus on the optimization of modeling methods, which ignore the impact of auxiliary features. This paper emphatically analyzes auxiliary features＇ impact on Qo S prediction such as service category and user location. To achieve this goal, in this paper designs and builds up a unified Qo S prediction framework based on Factorization Machines, which can incorporate multiple auxiliary features easily and conveniently. Combined with service category and user location, this paper develops a Qo S prediction method, which is proved to be advantageous via experiments on real-life data sets.
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