Classification vs. Regression - Machine Learning Approaches for Service Recommendation Based on Measured Consumer Experiences

Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first approach focuses on classification, predicting the best performing service, while the second approach focuses on regression, predicting service performances which can then be used for the determination of the best candidate. We assessed and compared both approaches for service recommendation w.r.t. The performance gain when selecting the recommended instead of a random service. Our evaluation is based on data measured on real Web services as well as on simulated data. The latter is needed for a more profound analysis of the strengths and weaknesses of each approach. The simulated data has similar statistical properties as the data measured on real Web services. In the real-world case, regression achieved a response time gain of over 92% of the optimum and classification over 83%. In case of simulated data, we could achieve an overall gain of up to 95% using classification, while regression achieved 89%.