Personalized Web Service Recommender System ( Clustering using Location and Quality-of-Service Information )

Abstract

Web Services Are Integrated Software Components For The Support Of Interoperable Machine-To-Machine Interaction Over A Network. The Number Of Publicly Available Web Services Is Steadily Increasing On The Internet. This Increase In Availability Makes It Hard For A User To Select An Accurate Web Service Among A Large Amount Of Available Services. An Inappropriate Service Selection May Cause Many Problems. In This Paper, We Propose A Novel Collaborative Filtering-Based Web Service Recommender System To Help The Users Select Services With Optimal Quality-Of-Service (Qos) Performance. This Recommender System Employs Location Information And Qos Values To Cluster Users And Services, And Make Personalized Service Recommendation For Users Based On The Clustering Results. Compared With Existing Service Recommendation Methods, Our Approach Achieves Considerable Improvement On The Recommendation Accuracy.