QOS-BASED WEB SERVICE RECOMMENDATION: A SYSTEMATIC LITERATURE REVIEW
DOI:
https://doi.org/10.70454/JRIST.020204Keywords:
Web Service Recommendation, Quality of Service, QoS Prediction, Collaborative Filtering, Matrix Factorization, Deep Learning, Service ComputingAbstract
Web service recommendation has become an essential research area due to the rapid growth of cloud computing and service-oriented architectures. Functional similarity alone is insufficient for selecting appropriate web services because multiple services often provide identical functionality with varying Quality of Service (QoS) characteristics. QoS-aware recommendation systems assist users in selecting services based on attributes such as response time, availability, reliability, throughput, and reputation. This review presents a comprehensive analysis of existing QoS-based web service recommendation techniques, including collaborative filtering, matrix factorization, clustering, deep learning, graph-based models, and hybrid recommendation approaches. The paper compares existing methodologies, identifies their strengths and limitations, discusses publicly available QoS datasets and evaluation metrics, and highlights current research challenges and future directions. The review serves as a valuable reference for researchers developing next-generation intelligent web service recommendation systems.
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Copyright (c) 2026 Gajendra Singh, Shyamol Banerjee (Author)
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