A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction

Abstract

How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. However, existing LF model-based QoS predictors mostly ignore the user/service neighborhoods, or reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a latent factor (DCALF) model to implement highly accurate QoS predictions, where data-characteristic-aware indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Results on two real datasets demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors.