Citation

Mahanra Rao, Vijayalakshmi and Ramasamy, R Kanesaraj and Sayeed, Md Shohel (2025) BERT-Based Approaches for Web Service Selection and Recommendation: A Systematic Review with a Focus on QoS Prediction. Future Internet, 17 (12). p. 543. ISSN 1999-5903

Text BERT-Based Approaches for Web Service Selection and Recommendation_ A Systematic Review with a Focus on QoS Prediction.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Effective web service selection and recommendation are critical for ensuring high-quality performance in distributed and service-oriented systems. Recent research has increasingly explored the use of BERT (Bidirectional Encoder Representations from Transformers) to enhance semantic understanding of service descriptions, user requirements, and Quality of Service (QoS) prediction. This systematic review examines the application of BERT-based models in QoS-aware web service selection and recommendation. A structured database search was conducted across IEEE, ACM, ScienceDirect, and Google Scholar covering studies published between 2020 and 2024, resulting in twenty-five eligible articles based on predefined inclusion criteria and PRISMA screening. The review shows that BERT improves semantic representation and mitigates cold-start and sparsity issues, contributing to better service ranking and QoS prediction accuracy. However, challenges persist, including limited availability of benchmark datasets, high computational overhead, and limited interpretability of model decisions. The review identifies five key research gaps and outlines future directions, including domain-specific pre-training, hybrid semantic–numerical models, multi-modal QoS reasoning, and lightweight transformer architectures for deployment in dynamic and resource-constrained environments. These findings highlight the potential of BERT to support more intelligent, adaptive, and scalable web service management.

Item Type:	Article
Uncontrolled Keywords:	QoS prediction, semantic representation
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	07 Jan 2026 01:50
Last Modified:	07 Jan 2026 01:50
URII:	http://shdl.mmu.edu.my/id/eprint/15155

Downloads

Edit (login required)