Abstract
Quality-of-Service (QoS) describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ‘hybrid user-location-aware prediction based on weighted Adamic-Adar (WAA)’ (HUWAA) was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient (IPCC), user-based pearson correlation coefficient (UPCC) and Web service recommender system (WSRec) algorithms.
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