A study the web services using machine learning for personalized QOS are recommended
DOI:
https://doi.org/10.29070/ghy8xp78Keywords:
QoS, Machine Learning, Linear Regression, Prediction Accuracy, Web ServicesAbstract
This experiment addresses the challenge of predicting Quality of Service (QoS) values for web services using various linear regression methods, including fitrtree (binary regression decision tree), fit ensemble with LSBoost and Bag, and lasso regression. By leveraging past user experiences, these models aim to recommend web services based on predicted QoS values with a focus on minimizing prediction errors. Data for the study will be collected from real-world web services across diverse locations. Four linear regression models will be implemented, each evaluating prediction accuracy. Initial findings suggest that fit ensemble with Bag and lasso regression perform better in predicting QoS values with minimal accuracy deviations, making them more effective for recommending web services. Experiments will be conducted using a QoS dataset derived from millions of real-world web service interactions, involving web services from 29 countries and users from 31 countries. Predictions will cover all users to recommend 1,292 unique web services. Recommendations will be tailored for continent-specific users, providing regionally optimized services. The study includes an in-depth analysis of datasets, focusing on the country-wise and continent-wise distribution of web services and users.
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