Abstract
This paper studies the problem of designing practical web service recommender systems that make quality predictions based on web service invocation histories. We formalize a generic architecture for such systems, that include both an online training module and an off-line training module. In addition, we develop a general online and off-line training algorithms to demonstrate the advantages of such an architecture and the natural fit of stochastic gradient descent algorithms for the architecture. The advantages of our proposed architecture has been confirmed by the comparisons with existing web service recommendation algorithms on a real-life dataset.
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