Web Service QoS Prediction via Extended Canonical Polyadic-based Tensor Network

TL;DR

This paper introduces an Extended Canonical Polyadic-based Tensor Network model that improves web service QoS prediction accuracy by modeling user-service correlations in a low-dimensional space.

Contribution

It extends existing CP-based tensor models by incorporating user-service correlation modeling, enhancing QoS prediction performance.

Findings

ECTN outperforms state-of-the-art models in accuracy

Experiments on public QoS datasets validate effectiveness

Model captures user-service correlations effectively

Abstract

Today, numerous web services with similar functionalities are available on the Internet. Users often evaluate the Quality of Service (QoS) to choose the best option among them. Predicting the QoS values of these web services is a significant challenge in the field of web services. A Canonical Polyadic (CP)-based tensor network model has proven to be efficient for predicting dynamic QoS data. However, current CP-based tensor network models do not consider the correlation of users and services in the low-dimensional latent feature space, thereby limiting model's prediction capability. To tackle this issue, this paper proposes an Extended Canonical polyadic-based Tensor Network (ECTN) model. It models the correlation of users and services via building a relation dimension between user feature and service feature in low-dimensional space, and then designs an extended CP decomposition structure to improve prediction accuracy. Experiments are conducted on two public dynamic QoS data, and the results show that compared with state-of-the-art QoS prediction models, the ECTN obtains higher prediction accuracy.