Abstract
Accurately estimating unknown quality-of-service (QoS) data based on historical records of Web-service invocations is vital for automatic service selection. This work presents an effective scheme for addressing this issue via alternating direction method-based matrix factorization. Its main idea consists of a) adopting the principle of the alternating direction method to decompose the task of building a matrix factorization-based QoS-estimator into small subtasks, where each one trains a subset of desired parameters based on the latest status of the whole parameter set; b) building an ensemble of diversified single models with sophisticated diversifying and aggregating mechanism; and c) parallelizing the construction process of the ensemble to drastically reduce the time cost. Experimental results on two industrial QoS datasets demonstrate that with the proposed scheme, more accurate QoS estimates can be achieved than its peers with comparable computing time with the help of its practical parallelization.