Understanding the Role of AI in OTT Content Curation

How AI in OTT Content Curation Works

Streaming platforms today can hold thousands or even tens of thousands of titles, so without some help, you would spend more time searching than watching. AI-powered recommendation engines solve this by predicting what you'll enjoy, and there are three main techniques powering these recommendations:

Collaborative Filtering

Collaborative filtering looks at patterns across many users. If people who watched the shows you like also enjoyed a new series, the system will suggest it to you. It does not need to know details about the titles themselves; just who watched what.

Content-Based Filtering

Content-based filtering examines the items in detail. It builds profiles using genres, actors, directors, keywords, and even visual or audio features. If you liked one crime thriller with a specific lead actor, the engine finds others that share those traits.

Combination Filtering

Most modern platforms combine both of the above approaches into a hybrid system and layer in signals that respond to what a viewer's doing at the moment, like what device they're using, the time of day, how long they tend to pause, whether they finish a title, and even their search terms.

What This Means for Your Viewers

Early streaming services relied on human editors to create categories, but that approach could never scale to today's vast libraries and can't ever adapt to individual viewer tastes. AI is what has changed the game. Today, machine learning models train on billions of viewing events continuously, so their recommendations can shift instantly as a viewer's habits change or as new titles arrive.

Good curation saves viewers time and reduces their frustration. They open your app and immediately see options that feel relevant. For you, that translates to higher satisfaction rates and fewer cancellations.

Challenges to Manage as a Content Provider

AI is powerful, but it is not perfect, and simply turning everything over to the machine without management and oversight is a recipe for disaster. Training data can easily come to reflect past biases and over-represent certain genres or demographics, and this will mean the recommendations create echo chambers.

Privacy is another concern. The more data the system collects, the better the recommendations, but users rightly worry about how that data is being stored and used. Responsible platforms now offer controls to limit data use or reset a profile, and it pays to be transparent with your viewers to keep their trust. Finally, make sure you're including a diversity of recommendations. The leading streaming services always deliberately mix in titles outside the usual patterns to keep the experience fresh and to promote new content, and this is a smart move for any service.

If you run or want to build a streaming service and need tools to handle content upload, distribution, automation, and AI-powered personalization at scale, visit us at Lightcast.com and request a demo.