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Online Educational Video Recommendation System Analysis

Online Educational Video Recommendation System Analysis
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Author(s): Parvathi R. (Vellore Institute of Technology, Chennai, India), Aarushi Siri Agarwal (Vellore Institute of Technology, Chennai, India)and Urmila Singh (Vellore Institute of Technology, Chennai, India)
Copyright: 2023
Pages: 19
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch093

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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.

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