RAHN: A Reputation Based Hourglass Network for Web Service QoS Prediction

TL;DR

This paper introduces RAHN, a novel deep learning network utilizing reputation metrics and an hourglass architecture to improve the accuracy of Web service QoS prediction, addressing challenges posed by service homogenization.

Contribution

The paper presents a new reputation-based deep learning model with an hourglass network architecture for more accurate QoS prediction in Web services.

Findings

RAHN outperforms six baseline methods in MAE and RMSE.

Reputation calculation improves prediction accuracy.

Hourglass network effectively aggregates multi-scale features.

Abstract

As the homogenization of Web services becomes more and more common, the difficulty of service recommendation is gradually increasing. How to predict Quality of Service (QoS) more efficiently and accurately becomes an important challenge for service recommendation. Considering the excellent role of reputation and deep learning (DL) techniques in the field of QoS prediction, we propose a reputation and DL based QoS prediction network, RAHN, which contains the Reputation Calculation Module (RCM), the Latent Feature Extraction Module (LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user reputation and the service reputation by using a clustering algorithm and a Logit model. LFEM extracts latent features from known information to form an initial latent feature vector. QPHN aggregates latent feature vectors with different scales by using Attention Mechanism, and can be stacked multiple times to obtain the final latent feature vector for prediction. We evaluate RAHN on a real QoS dataset. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than the six baseline methods.