An Analysis for the Prediction of Prefetched Content on Social Media

Abstract

In Recently, social media networks are popularly emerging through world. This has been a great platform for information sharing through network among people. Being ubiquitous in nature, social media are accessible anywhere and at any point of time. To provide Quality of Experience support, a learning-based model for social media is been proposed. This is mainly used to improve user's usage satisfaction and to reduce access delay in Online Social Networks (OSN). For this, an analysis of Twitter traces for over fourteen months is conducted. Over 2,800 users Twitter data is collected using Twitter API for the analysis of social media friendship. And a cluster-based analysis for these set of user friends is made for the prefetch prediction. A mechanism using XGBoost algorithm and Decision Tree Regressor algorithm, the performance of this framework is been determined. The performance of this framework using trace-drive emulations on the social media network has been evaluated. Evaluation results could do superior performance with XG Boost algorithm of around 85.1% accuracy of access delay reduction.