Over-The-Top (OTT) platforms such as Netflix, Amazon Prime, and Disney+ relied heavily on complex recommendation algorithms to determine what content users see, shaping not only discovery but also overall viewing patterns in 2025.
These systems process vast amounts of user interaction data in real time, including what viewers watch, how long they watch, what they skip, their search queries, and even the time of day and device used, to build detailed, customised individual profiles. OTT recommendations engines combine multiple machine learning techniques to achieve this.
Collaborative filtering compares users’ habits with those of similar users to suggest content they liked. Content-based filtering matches individual preferences using details such as genre, cast, themes, and keywords. Matrix factorisation, a data analysis technique, extracts hidden likes from incomplete data, and deep learning networks spot subtle patterns to improve prediction accuracy.
For instance, Netflix runs dozens of models, weighs them, and tests combos to boost engagement. Today’s recommendations on streaming platforms rely on a few key things. They look at users’ viewing history and how they engage, such as whether they finish shows, skip them, or rewatch parts.
They match users or shows based on similarities and check content details such as genre, themes, and cast. Other factors include the device being used, the time of day, and their recent activity. For new users, early preferences help get things started without much data. These personalised picks cut down search time, increase watch hours, and keep users coming back happy.
OTT platforms say over 80% of viewing activity comes from the suggestions. However, issues persist. Algorithms trap users in filter bubbles, hide diverse content, and fail when profiles lack information. They collect a lot of data, sparking privacy concerns and leading to biased picks. As rivalry grows, how OTT services blend personalisation with fairness and clarity will define the next step for streaming.
