Machine Learning Applications in Cable TV Advertising: Usage and Challenges (2019)

By Srilal M Weerasinghe PhD, Charter Communications

Use of machine learning (ML) for image and video analyses would often include face recognition, personalization and recommendations. An emerging trend is the application of AI technology for TV advertising. In this paper, we present the unique challenges in applying machine learning to carrier-class video advertising. We focus the discussion on a specific use case that is common to all ad supported TV services.

The selected use case is Ad Ingest Quality Control (QC). In the United States, TV commercials are subjected to various rules and regulations. Ads containing specific content (e.g. Alcohol, firearms) are barred from airing during certain TV programs. Identifying these categories may pose a challenge, as off the-shelf machine learning products are more oriented towards facial recognition. That is to be expected perhaps, as the video ML products were primarily intended for surveillance and sports applications.

However, our research indicates that by judiciously combing metadata from multiple data streams, machine learning analysis results can be improved.

The intent of the paper is to outline the results and recommendations of a proof-of-concept study that will be helpful to the carrier-class video services community.

By clicking the "Download Paper" button, you are agreeing to our terms and conditions.

Similar Papers

MLaaS Applications in Digital Video: Supplanting Disliked Content
By Srilal Weerasinghe PhD, Charter Communications
Applications of Machine Learning in Cable Access Networks
By Karthik Sundaresan, Nicolas Metts, Greg White, Albert Cabellos-Aparicio, CableLabs
Explainable AI for Data Clean Room Query Validation
By Srilal Weera PhD, Charter Communications
Scaling IP Advertising Using Manifest Manipulation
By Vipul Patel, Charter Communications; Xavier Denis, CommScope
Best Practices for A/B Testing Machine Learning Models
By Piper Williams, Charter Communications; Ryan Lewis, Charter Communications; Miranda Kroehl, Charter Communications; Veronica Bloom, Charter Communications; Brock Bose, Charter Communications
Cable and Mobile Convergence: A Vision from the Cable Communities Around the World
By Jennifer Andréoli-Fang, PhD, CableLabs; John T. Chapman, Ian Campbell, & Mark Grayson, Cisco; Ahmed Bencheikh, Praveen Srivastava & Vikas Sarawat, Charter Communications; Drew Davis & Paul Blaser, Cox Communications; Damian Poltz & Dave Morley, Shaw Communications; Eduardo Panciera, Telecom Argentina; Philippe Perron, Sylvain Archambault, Eric Menu, Géraldine Trouillard & David Lagacé, Videotron; Gavin Young & Bruno Cornaglia, Vodafone
Operational Transformation Via Machine Learning
By Shamil Assylbekov, PhD. & Devin Levy, Charter Communications
Leveraging Machine Learning for Network Traffic Forecasting
By Diane Prisca Onguetou, PhD, Independent Consultant; Achintha Maddumabandara, Rogers Communications; Jeffrey Lee, Rogers Communications
Detecting Video Piracy with Machine Learning
By Matthew Tooley & Thomas Belford, NCTA – The Internet & Television Assocation
Terahertz Spectrum: Challenges, Potential And Applications
By Lakhbir Singh, Charter Communications
More Results >>