Utilizing AI for Content Recommendations in OTT Platforms

Viewers are picky, and they want content that feels handpicked for them. They'll bounce if they're wading through irrelevant videos. AI analyzes user behavior, like what they watch, for how long, and also what they skip, and then uses this to suggest content that keeps them glued. AI doesn't require a massive team; with the right tools, you can set it up and let it do all the heavy lifting.

Collaborative filtering is AI 101. It looks at what similar users like and recommends those to others. Say you've got a viewer who loves your woodworking tutorials. Collaborative filtering finds other people with similar tastes and recommends the videos they watched, like maybe one on advanced lathe techniques. You don't need to code this from scratch, as the AI does this for you. All you need is the right platform that can gather the viewer data and keep all your tools compatible and working together.

This method uses things like genres, tags, or descriptions and matches these tags to users' preferences. For instance, if someone watches Morning Yoga Flow, the system might suggest Evening Relaxation Yoga based on shared tags. Tools like Lightcast's analytics dashboard can help you tag efficiently. Pro tip: Regularly update your metadata, because stale tags lead to stale recommendations.

Hybrid systems combine user behavior and content metadata for sharper recommendations. Imagine a viewer who binges sci-fi shorts but also dabbles in horror. A hybrid AI might recommend a sci-fi horror crossover based on their watch history and content tags.

AI makes this doable for small setups. You just connect your catalog, feed in the user data, and it does the rest. Your job will be to do things like test different weightings. Maybe you want to lean 60% on user behavior and 40% on content tags, and then tweak that based on the engagement metrics as you see how people are responding.

AI isn't set-it-and-forget-it, at least in part because your viewers are people, and people can change their minds, adjust their habits, and be hard to predict. Run A/B tests to compare your recommendation sets. Show one group your AI's suggestions and another a random or manual list, then track the watch time and retention. If AI-driven recommendations are boosting engagement, roll them out fully. If not, tweak your parameters. Above all, be patient, as it usually takes a few rounds to get it right.