An Over-The-Top Video Streaming Platform Enhanced with Artificial Intelligence

Abstract

Presently, over-the-top video streaming services have gained a lot of popularity among movie and television show viewers. This is because current over-the-top video streaming services offer hassle-free, easily accessible and affordable subscription-based platforms to consume thousands of entertainment contents with clicks of a few buttons. But due to the vast content pool indexing, discovering, searching and recommending a particular content in a streaming platform is a very onerous task. Most of the time an online video streaming platform user will be spending most of his time “browsing what to watch '' rather than enjoying a piece of content. “Entertainment” should be a hassle-free process because it is a getaway for people to take a break from their work to relax and enjoy. But because browsing an entertainment piece is a hefty task in these current streaming platforms most of the users have begun to abandon or move to other streaming platforms or to traditional methods of movie/television content watching. From a business point of view this will ultimately lead to a huge financial loss in these streaming platforms. With the help of the conversational artificial intelligence which will be built upon the author’s developed streaming platform, a user can get to establish a conversation with the platform to unravel his entertainment needs, in a similar way that he would do conversations with a friend of him about a certain movie or movie genre that he would like watch. By examining those conversations that the user had with the conversational AI and the user patterns, developed machine learning models will be able to make personalized content recommendations to a particular user. In addition to that using artificial intelligence and machine learning algorithms content sorting, content recommendation and the content discovery will be engineered to the most efficient level.