Journal article Open Access
Shanmuga Skandh Vinayak E; Venkatanath A G S; Shahina A; Nayeemulla Khan A
Ever since its early inception in the year 2005, YouTube has been growing exponentially in terms of personnel and popularity, to provide video streaming services that allow users to freely utilize the platform. Initiating an advertisement based revenue system to monetize the site by the year 2007, the Google Inc. based company has been improving the system to provide the users with advertisements on them. In this article, 7 recommendation engines are developed and compared with each other, to determine the efficiency and the user specificity of each engine. From the experiments and user-based testing conducted, it is observed that the engine that recommends advertisements utilizing the objects and the texts recognized, along with the video watch history, performs the best, by recommending the most relevant advertisements in 90% of the testing scenario.
|Data volume||18.8 MB|