Implementing Personalized Recommendations in OTT Platforms

Implementing Personalized Recommendations in OTT Media

Implementing personalization involves data collection, and that means you need machine learning models, real-time processing, and attention to the important ethical considerations.

The Right Data Collection

The foundation of personalized recommendations is the insights you get from viewer data. You need to collect everything from ratings, likes, and other explicit data to those implicit signals like viewing duration, skips, and search history. All this needs to be done via privacy-compliant data pipelines that follow all regulations, like the updated GDPR and emerging AI ethics laws, and know how to use the right anonymization techniques to aggregate this information.

Your core recommendation algorithms fall into three categories: collaborative filtering, content-based filtering, and hybrids.

Collaborative Filtering

Collaborative filtering identifies patterns across users. For example, if two users both enjoy series like "The Mandalorian," the system would suggest cross-recommendations using techniques like user-item matrix factorization via algorithms such as Alternating Least Squares (ALS).

Content-Based Filtering

Content-based filtering analyzes your metadata, like the genres, actors, or plot keywords that are chosen, and then employs NLP models to generate embeddings for similarity matching.

Hybrid Filtering

Hybrid approaches are becoming dominant in 2025, and these combine the first two filters with deep learning to get the best possible accuracy. The idea here is to balance giving viewers familiar suggestions that you know they'll like with some exploratory ones that allow them to discover new things.

Advancements in AI and machine learning have propelled these systems forward and now even allow you to make recommendations in real-time based on contextual factors like the time of day, the viewer's precise location, and even weather data. For example, say you've got a user streaming on a rainy evening. They might be more inclined to see some cozy drama than an action film, and your machine learning model is smart enough to realize this.

A/B Testing

A/B testing is very important to properly implementing recommendations that your viewers will find engaging. They're also a great way to compare baseline models to those enhanced by AI to find out if the AI is working or needs to be tweaked. Your feedback mechanisms can then retrain models incrementally; for instance, if a user skips a recommendation, it's downvoted in the next update cycle.

There's a lot that goes into making great recommendations that your viewers will love. For a smaller platforming, doing it right can require more effort and time than you're able to give; but with the right help, even a startup can excel. At Lightcast.com, we offer an OTT platform for your media that's completely customizable. We're your all-in-one stop for monetization, personalization, and ROI-maximization, so contact us at Lightcast.com today to see a demo and learn more.