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. In this paper, a new hybrid user-location-aware prediction based on WAA (HUWAA) is proposed. The implicit neighbor search is optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem is validated in experiments on public real world datasets. The new algorithms outperform the existing IPCC, UPCC and WSRec algorithms.
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