Gated Multi-task Learning for QoS-aware Service Recommendation

Abstract

In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very interested in. A key challenge is how to get personalized and accurate QoS values of web services from the multi-source information of users/services. In addition, complex contexts and multi-tasking attributes also make the collaborative services quality prediction difficult to meet. Inspired by the progress of multi-task deep learning, this paper proposes a new framework of service quality prediction called multi-task gated expert network (MGEN), which is based on gating mechanism and multi-task learning. The MGEN consists of three parts: expert-based context-source feature extraction, gated interaction learning and information fusion. First, multiple expert networks are used to capture the latent representation in raw data of web services, including both implicit features in the QoS matrix and explicit features of service descriptions. In the process, several independent experts extract features from different dimensions. Second, output embedding of different expert networks is transmitted to each task layer through different weights, learning both global and local high-order feature interactions. Third, the interactive information of different tasks is fused by embedding splicing, and then fed back to the prediction module for final service recommendation. Results of the comparative experiment demonstrate that the proposed MGEN significantly outperforms the state-of-the-art models of service recommendation, where both the implicit and explicit information of various multi-source features and interaction sequences are effectively extracted. In addition, the gating mechanism performs well in the QoS prediction by independently modeling each task.