Modeling user's non-functional preferences for personalized service ranking
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Personalised graph-based selection of web APIs
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
International Journal of Web Services Research
QoS-aware service selection via collaborative QoS evaluation
World Wide Web
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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.