Affiliation:
1. Vellore Institute of Technology, Chennai, India
Abstract
Most online platforms that provide video content, including TEDx, usually use various recommendation systems to gather more viewers. These videos are recommended based on various criteria. They can be either based on the user behavior and history of watched videos or on the basis of generally liked videos. The aim of this work is to conduct an in-depth analysis of the education platform called TEDx. This analysis will help in deriving the current protocols and thresholds this platform follows to curate and recommend videos to new users of the platform. The end goal is to figure out the various correlations between different parameters pertaining to these videos and on this basis to derive concrete illustrative representations of said relations and also to build a framework around these facts to find the exact relation between various videos on the platform.