Matrix factorization-based Web service Quality of Service (QoS) prediction has emerged as a powerful approach for improving the performance and usability of web services. This paper explores the methods and applications of matrix factorization-based QoS prediction, presenting a comprehensive overview of its techniques and real-world implementations. In the domain of methods, we delve into the description of matrix factorization models, emphasizing their ability to capture latent patterns in QoS data. Furthermore, we discuss the selection and evaluation of input data, highlighting the importance of rigorous data preprocessing and validation methodologies. The training and testing procedures are elucidated, showcasing the iterative optimization algorithms and evaluation metrics employed to assess model performance. Additionally, we delve into optimization techniques and algorithms, illustrating how gradient descent, ALS, and regularization methods enhance prediction accuracy and robustness. In terms of applications, we present case studies demonstrating the effectiveness of matrix factorization-based QoS prediction in diverse domains such as e-commerce, streaming services, and transportation systems. Real-world applications and scenarios are explored, showcasing the versatility and adaptability of matrix factorization-based methods in optimizing service delivery and user experiences. Lastly, we discuss the comparison with other QoS prediction methods, highlighting the strengths and limitations of Matrix Factorization approaches and identifying emerging trends in QoS prediction for web services, including the integration of deep learning techniques, federated learning, and fairness-aware modeling. Through a holistic examination of methods and applications, this paper provides valuable insights into the current state and future directions of matrix factorization-based Web service QoS prediction.

Zhenzhen Liu. Matrix Factorization-based Web Service QoS Prediction: Methods and Applications. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 3: 146-151. https://doi.org/10.25236/AJETS.2024.070320.

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