A Hybrid Collaborative Filtering Technique for Web Service Recommendation using Contextual Attributes of Web Services

Noor Ul Ain, Ali Irfan, N. Iltaf, Mahmood ul Hassan
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Abstract

Quality of Service (QoS) Aware recommender system considers the quality of service to recommend personalized web services to the user. Quality of Service parameters also includes response time and throughput that a user receives when invoking a web service. There exist numerous collaborative filtering techniques that tend to predict Quality of Service value; however, existing techniques only use the client-side information of QoS and neglect the service's contextual attributes. This paper proposes a new Web Service Recommendation System that will consider the contextual attributes of Web services. The proposed method collects the contextual properties from WSDL files to cluster Web services based on their attribute similarities. Thus, more accurate neighbour selection takes place and prediction value is determined using QoS record; in addition to this, a user-influenced prediction value is also determined. To map both, service, and user influence on QoS prediction, a hybrid memory-based CF model is developed. The effectiveness and reliability of the proposed system is verified by the results of experiments.
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基于Web服务上下文属性的Web服务推荐混合协同过滤技术
QoS (Quality of Service)感知式推荐系统考虑服务质量向用户推荐个性化的web服务。服务质量参数还包括用户在调用web服务时接收到的响应时间和吞吐量。存在许多倾向于预测服务质量价值的协同过滤技术;然而,现有的技术只使用QoS的客户端信息,而忽略了服务的上下文属性。本文提出了一种考虑Web服务上下文属性的Web服务推荐系统。提出的方法从WSDL文件收集上下文属性,并根据属性相似性对Web服务进行集群。这样,可以更准确地选择邻居,并利用QoS记录确定预测值;除此之外,还确定了用户影响的预测值。为了映射服务和用户对QoS预测的影响,开发了一种基于内存的混合CF模型。实验结果验证了该系统的有效性和可靠性。
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