Collaborative filtering based service ranking with invocation histories

Collaborative filtering based recommender systems have been 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 thesis, we propose a novel collaborative filtering based service ranking mechanism, in which the invocation and query histories are used to infer the users’ preferences, and user similarity is calculated based on 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 the simulated data proves the effectiveness of the proposed algorithm.