Recommendation Engine

Showcase your audience what they want to see

One Touch Enablement

Effortlessly integrate the recommendation engine with Muvi One. Simply enable it within the "Manage Content" section of your Muvi One CMS. Unleash the potential of artificial intelligence with ease.

Activity-based Recommendation

Enhance user engagement by recommending personalized content based on their daily interactions. Muvi One's recommendation engine utilizes machine learning to analyze user behavior, presenting their favorite content upon login, optimizing click-through rates and streaming.

Choice-based Recommendation

Leverage the power of the Muvi One recommendation engine, developed by seasoned data scientists with extensive expertise in studying user behavior across top OTT platforms. Allow the advanced algorithm to analyze each user's data and curate tailored content based on their preferences and interests.


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  • AI-powered recommendation engine
  • Allow payments via valid mobile number
  • Mobile API for Apps
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No Background Building

Muvi One's robust recommendation engine eliminates the need for manual data insertion from your end. Users receive tailored content suggestions based on their streaming history, with the advanced algorithm analyzing past user behavior upon activation to seamlessly recommend ideal content to users without manual data uploads.

Advanced Algorithms

The recommendation engine, driven by Alie, harnesses cutting-edge algorithms within the recommender system landscape. We employ collaborative filtering, deep learning, and content-based algorithms to deliver precise content recommendations tailored to each user.

End-user Interaction

Muvi One's end-user interaction recommendations leverage the user's historical engagement on your platform, encompassing likes, purchases, views, ratings, and browsing history. Employing content-based filtering, the system comprehends user behavior towards item attributes that elicit positive responses. Upon understanding user behavior, Muvi One establishes links between users, items, and their attributes, facilitating tailored recommendations accordingly.