Media Companies Leverage Data-Driven AI to Evolve, Prosper

The media industry’s interest in artificial intelligence goes much deeper than simply portraying its implications in movies such as “Her” or “Ex Machina.” Recommendations and push notifications are just two examples of how media uses AI. YouTube has evolved its use of machine learning algorithms to improve its content recommendations. In the early days, the site used “collaborative filtering” to feed videos to viewers. Now the company uses much more complex models based on deep learning powered by neural networks. Continue reading Media Companies Leverage Data-Driven AI to Evolve, Prosper

New York Times’ New Algorithm Improves Recommendation Engine

The New York Times is in the process of tweaking its recommendation engine by integrating two previously used models. The Recommended for You section of NYT provides suggested content from over 300 articles, blog posts and interactive stories that are published every day. By personalizing the content that appears on apps and the website, readers are directed to stories that have the greatest interest and relevancy to them. NYT described its efforts to rebuild the engine for maximum efficiency and accuracy. Continue reading New York Times’ New Algorithm Improves Recommendation Engine

Spotify Intern Creates System to Improve Recommendations

By analyzing the acoustic properties of songs on Spotify, intern and PhD student Sander Dieleman hopes to advance the streaming service’s recommendation algorithms to aid users in discovering new and lesser known music. Rather than basing recommendations on the choices people with similar tastes make, they would be based on songs the user listens to. This method, which requires deep learning, would then mix more obscure but user relevant songs into the recommendations. Continue reading Spotify Intern Creates System to Improve Recommendations