A Recommendation System for Decentralized Autonomous Organization

Structured data analysis has historically achieved remarkable success. However, the analysis of massive amounts of unstructured video data is still a challenging problem. Over one billion people use YouTube, a Google corporation, which generates billions of views. YouTube data is being created in extremely large quantities, and with a massive demand to store, analyze, and carefully study large amounts of data to make it usable for big data analytics. For the analysis of these YouTube data, the absence of YouTube Shorts which has become a current trend is the limitation. To address this limitation, YouTube Shorts along with traditional duration videos have been considered for analysis in this work. For analyzing these data, various Machine Learning (ML) techniques like clustering and classification have been considered to categorize the content creators into three different categories such as highly rated, moderately rated and lowly rated. These ratings for recommendation assist content creators in enhancing the value of their brand and the users are benefited by consuming, exchanging shares and promoting the content. The results observed show that the accuracy of the Random Forrest and Gradient Boosting classifiers have equivalent performance i.e., around 98% which are suitable for the above recommendation system. Hence, the objective to encourage investors and fans to become active stakeholders and owners of the creator’s micro-economy is addressed in this paper.