Robust web service recommendation via quantile matrix factorization

We study the problem of personalized Quality of Service (QoS) estimation for web services. State-of-the-art methods use matrix factorization or collaborative prediction to estimate web service response times and throughput for each user based on partial measurements collected from past invocations. We point out that in reality, both the response times and through-put of web services follow highly skewed distributions. In this case, the conditional mean QoS estimates generated by traditional matrix completion approaches can be heavily biased toward a few outliers, leading to poor web service recommendation performance. In this paper, we propose the Quantile Matrix Factorization (QMF) technique for web service recommendation by introducing quantile regression into the matrix factorization framework. We propose a novel and efficient algorithm based on Iterative Reweighted Least Squares (IRLS) to solve the QMF problem involving a non-smooth objective function. We further extend the proposed QMF approach to take into account user and service side attributes. Extensive evaluation based on a large-scale QoS dataset has shown that our schemes significantly outperform various state-of-the-art web service QoS estimation schemes in terms of personalized recommendation performance.