Personalized and Accurate QoS Prediction Approach Based on Online Learning Matrix Factorization for Web Services

Abstract

Quality of Service (QoS) prediction has played an important role in service computing. However, in the real-world scenario of Web service, many user-observed QoS values are unknown and vary over time. In order to provide high accurate and efficient QoS prediction performance for Web services, we propose a personalized and accurate QoS prediction approach namely PAOMF. Our prediction model is built by employing matrix factorization and online stochastic gradient descent algorithm. Extensive experiments are conducted on real world public datasets, which demonstrate the effectiveness and efficiency of our proposed approach.

Conditioned Source Separation by Attentively Aggregating Frequency Transformations With Self-Conditioning

“…Increasing |S L | from 4 to 16 tends to improve the performance of every architecture. However, having too many latent sources degrades SDR performance, similarly to the existing latent component analysis-based solutions in different domains [44,45]. Especially, models with |S L | > 16 tend to generate more artifacts.…”