关键词: Web服务, 聚类, 服务质量预测, 个性化, 推荐

Abstract: With the number increasing of Web services, recommending and selecting the personalized Web services for consumers has become one of the most important challenges in the field of service computing. This paper studies the approach for personalized Web services recommendation and proposes a model-based and memory-based quality of service (QoS) prediction approach for Web services. In the proposed approach, the consumers’ expectation, rating and the QoS information are quantified. And based on the objective QoS data and the subjective rating, the quality of services is predicted by clustering, mapping and aggregation. The result is a list of recommended services for selection. In addition, this paper designs a prediction framework, and realizes the information collecting and processing and personalized Web services recommendation through the framework. The experimental results demonstrate that compared with most other service recommendation approaches, the proposed approach increases the accuracy of recommendation results.

Key words: Web service, clustering, QoS prediction, personalization, recommendation