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A Study of Predictive Caching Using Local Storage and Machine Learning in Web Applications
Subject area: Science,Engineering and Technology · Area of research: ML-Based Predictive Caching
DOI: https://doi.org/10.64388/IREV9I11-1717631
Abstract
This study explores improving web performance using Machine Learning–based predictive caching with localStorage. Traditional caching only stores previously visited content, which limits performance for first-time users. The proposed system predicts user behavior and preloads likely content in advance. The study compares no caching, traditional caching, and ML-based caching using metrics like LCP, FCP, and TTI. Results show significant improvements, with faster load times and higher cache efficiency using predictive caching. Overall, predictive caching provides a more efficient and responsive web experience than traditional methods.
Keywords
Localstorage, Predictive Caching, Machine Learning, Web Performance, LCP, FCP, TTI, Client-Side Caching, User Behavior Prediction.
References
[1] Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the ACM SIGKDD Conference.
[2] Krishna, K. (2025). Advancements in Cache Management: A Review of Machine Learning Innovations. Frontiers in Artificial Intelligence.
[3] Li, C. et al. (2023). Predictive Edge Caching Using Sequential Learning Models. ScienceDirect.
[4] Rajan, P. K. (2023). Predictive Caching in Mobile Streaming Applications Using Machine Learning.
[5] Jose, J., & Ramasubramanian, N. (2022). Applying Machine Learning to Enhance Cache Performance. Evolutionary Intelligence.
[6] Lempel, R., & Moran, S. (2003). Predictive Caching and Prefetching of Query Results in Search Engines. World Wide Web Conference.
[7] IBM. (2024). What is XGBoost?\
[8] Ayush Wange (2025), Improving E-commerce Web Performance through Lazy Loading and Client-Side Caching.
How to cite this paper
@article{1717631,
author = {Ayush Wange, Dr. Netraja Mulay},
title = {A Study of Predictive Caching Using Local Storage and Machine Learning in Web Applications},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1535-1538},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717631.pdf},
abstract = {This study explores improving web performance using Machine Learning–based predictive caching with localStorage. Traditional caching only stores previously visited content, which limits performance for first-time users. The proposed system predicts user behavior and preloads likely content in advance. The study compares no caching, traditional caching, and ML-based caching using metrics like LCP, FCP, and TTI. Results show significant improvements, with faster load times and higher cache efficiency using predictive caching. Overall, predictive caching provides a more efficient and responsive web experience than traditional methods.},
keywords = {Localstorage, Predictive Caching, Machine Learning, Web Performance, LCP, FCP, TTI, Client-Side Caching, User Behavior Prediction.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717631}
}