Fuzzy Information Entropy and Region Biased Matrix Factorization for Web Service QoS Prediction
Guoxing Tang, Yugen Du, Xia Chen, Yingwei Luo, Benchi Ma
TL;DR
This paper addresses QoS prediction for numerous web services by enhancing matrix factorization with region-aware bias and a novel neighbor-selection mechanism. It introduces FIEMF, which uses fuzzy information entropy to measure user rating uncertainty, derives a region-biased center, selects top-K neighbors, and incorporates neighborhood regularization into MF via a linear combination with a region bias term. The method demonstrates stronger predictive accuracy than several state-of-the-art baselines on WS-DREAM, especially as data density increases from 5% to 20%. The approach marries global latent factors with local neighborhood and non-interactive effects, offering a practical improvement for QoS-based service recommendation in realistic network environments.
Abstract
Nowadays, there are many similar services available on the internet, making Quality of Service (QoS) a key concern for users. Since collecting QoS values for all services through user invocations is impractical, predicting QoS values is a more feasible approach. Matrix factorization is considered an effective prediction method. However, most existing matrix factorization algorithms focus on capturing global similarities between users and services, overlooking the local similarities between users and their similar neighbors, as well as the non-interactive effects between users and services. This paper proposes a matrix factorization approach based on user information entropy and region bias, which utilizes a similarity measurement method based on fuzzy information entropy to identify similar neighbors of users. Simultaneously, it integrates the region bias between each user and service linearly into matrix factorization to capture the non-interactive features between users and services. This method demonstrates improved predictive performance in more realistic and complex network environments. Additionally, numerous experiments are conducted on real-world QoS datasets. The experimental results show that the proposed method outperforms some of the state-of-the-art methods in the field at matrix densities ranging from 5% to 20%.