Open AccessAdvertisement Recommendation Engine - Improving YouTube Advertisement Services
Author(s) -
Shanmuga Skandh Vinayak E,
A G S Venkatanath,
A Shahina,
Nayeemulla Khan A
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d4846.119420
Subject(s) - popularity , computer science , revenue , advertising , recommender system , world wide web , business , psychology , social psychology , accounting
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.