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Author : Sandeep Chandak ;
Date : 15 Nov 2022
Over-the-top (OTT) video platforms have become increasingly popular in recent years, providing viewers with a vast selection of content to choose from. However, with so much content available, it can be difficult for users to decide what to watch.
To address this issue, many OTT video platforms use artificial intelligence (AI) and machine learning (ML) to personalise the user experience by tailoring content to individual preferences.
AI and ML technologies are used to personalise content in various ways, such as collaborative, content-based, and hybrid filtering. Collaborative filtering analyses the viewing history and preferences of similar users to generate content recommendations, while content-based filtering suggests content based on the attributes of the content itself. Hybrid filtering combines the two approaches to generate more accurate and diverse recommendations.
One of the most well-known examples of personalisation using AI and ML is Netflix. To generate personalised content recommendations, Netflix uses a recommendation engine that analyses user preferences, viewing habits, and ratings.
The recommendation engine takes into account various factors such as the time of day, day of the week, and even the user’s location to suggest content that is likely to be of interest to them. Amazon Prime is another example of an OTT platform that uses machine learning algorithms to personalise content recommendations based on user behaviour and preferences.
Personalisation provides several benefits to OTT video platforms, including increasing user engagement and satisfaction, enhancing user retention, and improving monetisation opportunities through increased user engagement with content. Personalisation can help OTT platforms stand out from their competitors by providing a unique and enjoyable user experience. It also helps increase user loyalty and brand advocacy, leading to increased revenue over time.
AI and ML technologies are also being used to improve the quality of the user experience in other ways. For example, machine learning can be used to optimise video encoding, reducing data usage and improving video quality, resulting in a better user experience. This approach is used by Amazon Prime, which uses machine learning to optimise its video encoding and improve the user experience for its viewers.
In conclusion, personalisation is becoming increasingly important for OTT video platforms to enhance the user experience and stay ahead of the competition.
AI and ML technologies effectively personalise content recommendations and make the user experience more enjoyable and relevant to individual preferences. As personalisation continues to evolve, we can expect more OTT platforms to adopt these technologies to improve their services and stay competitive.
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