Advertisement Recommendation Engine -Improving YouTube Advertisement Services
Shanmuga Skandh Vinayak E1,Venkatanath A G S2, Shahina A3, Nayeemulla Khan A4
1Shanmuga Skandh Vinayak E*, Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
2Venkatanath A G S, Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
3Shahina A, Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
4Nayeemulla Khan A, School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 115-119 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4846119420 | DOI: 10.35940/ijrte.D4846.119420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Advertisement, Recommendation Engine, Objects, Texts, Audio, Recognition, Detection, YOLO, Tesseract.