Collaborative Filtering Based Service Ranking Using Invocation Histories

Collaborative filtering based recommender systems are very successful on dealing with the information overload problem and providing personalized recommendations to users. When more and more web services are published online, this technique can also help recommend and select services which satisfy users´ particular Quality of Service (QoS) requirements and preferences. In this paper, we propose a novel collaborative filtering based service ranking mechanism, in which the invocation and query histories are used to infer the user behavior, and user similarity is calculated based on similar invocations and queries. To overcome some of the inherent problems with the collaborative filtering systems such as the cold start and data sparsity problem, the final ranking score is a combination of the QoS-based matching score and the collaborative filtering based score. The experiment using a simulated dataset proves the effectiveness of the algorithm.