Improved Active Web Service Recommendation Based on Usage History

With the increasing adoption and presence of Web services, h ow to recommend Web services to users that satisfy their potential functional and non-functional requirements eff ectively has become an important and challenging research i ssue. In this paper, we propose an enhanced Web service recommendation approach , named iAWSR (improved Active Web Service Recommendation) , that explores service usage history of users to actively rec ommend Web services for them. In iAWSR, we propose new method s for computing functional similarity and non-functional simil arity of Web service candidates, and a hybrid metric of simil arity is developed by combining the two sources of similarity measurement. iAW SR then ranks publicly available Web services based on value s of the hybrid metric of similarity, so that a top-k Web service reco mmendation list is generated for the user. We propose an effe ctive overall evaluation metric to evaluate our improved approach. Large -scale experiments based on real-world Web service dataset s re conducted. Experimental results show that iAWSR outperforms the exist ing approach AWSR on Web service recommendation performanc e.

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