Abstract
The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods.
Highlights
Powered by the advanced technology of Internet, web services with various functions improve the lives of common people [1,2]
The basic assumptions of the web service recommendation method are [6]: (1) Users prefer a service and its similar services; (2) Users prefer services that are used by other users with similar backgrounds and preferences; (3) Users prefer a service with certain characteristics as well as other services with similar characteristics
The web service recommendation is widely used in office automatic (OA), internet of vehicles, and tourism services
Summary
Powered by the advanced technology of Internet, web services with various functions improve the lives of common people [1,2]. Web users and and services services will propose a new Webservice servicerecommendation recommendationmodel model based based on on users clustering (RMUSC), which will combine user and service factors to obtain higher accuracy. Web clustering (RMUSC), which will combine user and service factors to obtain higher accuracy Web service service recommendations. We similar useruser clusters and services clusters by a collaborative filtering matrix factorization Weexploit exploit similar clusters and services clusters by a collaborative filtering matrix and obtain potential user preferences to generate recommendations. We develop a novel recommendation method by jointly considering the QoS of users and service clustering, whereby improving the accuracy of the recommendation.
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