Open AccessStatistical Measures for Trending Video Analysis using knowledge pre-processing and preliminary knowledge Analysis
Author(s) -
Rahul Pradhan,
Akansha Saxena,
Divyanshi Bansal,
Anupam Jain
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1131/1/012008
Subject(s) - computer science , world wide web , page view , information retrieval , web page , web navigation , static web page
YouTube has an algorithm that decides which videos will appear in the Trending Videos List. Some are more likely to be on the trending list but some videos which were never estimated to be on-trend are seen, it targets viewers’ attention over a short span. On Youtube, trending video lists are not personalized according to a person’s views, likes, or dislikes just like what the recommendation system does, rather its algorithm targets a collective group of people, the same Trending video page is displayed to everyone in a country. In India, the Trending page of. Like various recommendation systems, YouTube also gives you suggestions on your Home page based on your previously watched content. Recommendation systems are so common that we are using it without even knowing it, it makes the user experience much better. Common Examples are Amazon, NetFlix, and now Spotify uses it to suggest to us the music we might love to listen to. The trending videos page updates itself every few minutes (15 minutes). After every update, videos may move up, down, or stay in the same position in the list. Here in this Paper, we used statistical skills to analyze what are the reasons for a video to be on trends. In this paper, we have worked on a dataset for the year 2020. For data collection, we used a Web Scraper script, that’s not part of our work. Based on EDA we can say what is the exact reason for a video to appear on the trending page.