关键词: 服务计算, Web服务, 协同过滤, 服务质量, 用户群体

Abstract: The sparse data of Web services Quality of Service (QoS) which is invoked by service users in Web service recommendation may lead to low recommendation quality. In order to solve the problem, a collaborative filtering based Web service Recommendation algorithm for User Group (WRUG) was proposed. Firstly, personalized similar user group was constructed for each service user according to user similarity matrix. Secondly, instead of the group, the center of similar user group was employed to compute the user group similarity matrix. Finally, Web service recommendation equation with user group was defined and missing QoS values of Web service were predicted for target user. And a dataset was used for experiments which included 1.97 million real-world Web QoS invocation records. Compared with Traditional Collaborative Filtering algorithm (TCF) and Collaborative Filtering recommendation algorithm Based on User Group Influence (CFBUGI), the mean absolute error of the proposed WRUG was decreased by 28.9% and 4.57% respectively, and the coverage rate of WRUG was increased by 110% and 22.5% separately. The experimental results show that the proposed WRUG can not only achieve better prediction accuracy of Web service recommendation system, but also noticeably enhance the percentage of valuable predicted QoS values under the same experimental settings.

Key words: service computing, Web service, collaborative filtering, Quality of Service (QoS), user group