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Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction
IEEE Access ( IF 3.9 ) Pub Date : 2020-01-01 , DOI: 10.1109/access.2020.3022891
Limin Shen , Maosheng Pan , Linlin Liu , Dianlong You , Feng Li , Zhen Chen

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, constructing agile Web applications in heterogeneous environments conveniently. Web application interface (API) as an emerging technique attracts more and more enterprises and organizations to publish their deep computing functionalities and big data on the Internet, Web API has become the backbone to promote the development of SOC, thus forming the prosperous Web API economy. However, the number of available Web APIs on the Internet is massive and growing constantly, which causes the Web API overload problem. Quality of service (QoS) as an indicator is able to well differentiate the quality of Web APIs and has been widely applied for high quality Web API selection. Since testing QoS for massive Web APIs is resource-consuming, and the QoS performance depends on contextual information such as network and location, hence accurate QoS prediction has become very crucial for personalized Web API recommendation and high quality Web application construction. To address the above issue, this paper presents a context aware deep factorization machine model (CADFM for short) for accurate Web API QoS prediction. Specifically, we first carry out detailed data analysis using real-world QoS dataset and discover a positive relationship between QoS and contextual information, which motivates us to incorporate beneficial contexts for enhancing QoS prediction accuracy. Then, we treat QoS prediction as a regression problem and propose a context aware CADFM framework that integrates the contextual information via embedding technique. Particularly, we adopt MF and MLP for high-order and nonlinear interaction modeling, so as to learn the complex interaction between users and Web APIs accurately. Finally, the experimental results on real-world QoS dataset demonstrate that CADFM outperforms the classic and the state-of-the-art baselines, thereby generating the most accurate QoS predictions and increasing the revenue of Web APIs recommendation.

中文翻译:

上下文提高准确性:用于 Web API QoS 预测的上下文感知深度分解机建模

面向服务的计算 (SOC) 承诺了一个松散连接的协作服务的世界,可以方便地在异构环境中构建敏捷的 Web 应用程序。Web应用程序接口(API)作为一种新兴技术,吸引越来越多的企业和组织在互联网上发布其深度计算功能和大数据,Web API已成为推动SOC发展的中坚力量,从而形成繁荣的Web API经济. 然而,互联网上可用的Web API数量庞大且不断增长,导致Web API过载问题。服务质量(QoS)作为一个指标,能够很好的区分Web API的质量,被广泛应用于高质量的Web API选择。由于为大量 Web API 测试 QoS 是一种资源消耗,QoS 性能取决于网络和位置等上下文信息,因此准确的 QoS 预测对于个性化 Web API 推荐和高质量 Web 应用程序构建至关重要。为了解决上述问题,本文提出了一种上下文感知深度分解机器模型(简称 CADFM),用于准确的 Web API QoS 预测。具体来说,我们首先使用真实世界的 QoS 数据集进行详细的数据分析,并发现 QoS 与上下文信息之间的正相关关系,这促使我们结合有益的上下文来提高 QoS 预测的准确性。然后,我们将 QoS 预测视为回归问题,并提出了一个上下文感知 CADFM 框架,该框架通过嵌入技术集成了上下文信息。特别,我们采用MF和MLP进行高阶非线性交互建模,从而准确地学习用户与Web API之间的复杂交互。最后,真实世界 QoS 数据集的实验结果表明 CADFM 优于经典和最先进的基线,从而生成最准确的 QoS 预测并增加 Web API 推荐的收入。
更新日期:2020-01-01
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