Building a Movie Recommendation Engine for Smart TV App

    Key Details

    Improved movie recommendation function


    Deliver fresh personalized recommendations to every user


    Custom recommendation engine powered by machine learning

    Technologies and tools

    Python, SQL, Spark, Nginx, Flask-API, PostgreSQL, Spark Parquet

    Spotlight on Our Client

    The Client is a key player in the Smart TV digital home entertainment industry. Renowned for its premium video-on-demand service, it enables users to access newly released movies in exceptional quality or delve into a diverse library of over 7000 titles. With an intuitive Smart TV application at its core, the Client has cultivated a dedicated community, amassing an impressive 1.5 million monthly active users who regularly engage with their platform for their entertainment needs.

    The personalization Challenge

    The challenge is to upgrade from a manual recommendation list to a dynamic, personalized recommendation engine. By offering personalized suggestions, the platform can increase sales, improve customer satisfaction, and foster long-term loyalty.

    Crafting a Custom Recommendation Engine

    Personalized Recommendations

    Enhanced User Engagement

    Scalable Performance

    Measurable Impact

    Our custom movie recommendation engine has yielded significant outcomes, revolutionizing user experience and engagement in the following ways:


    Personalized Recommendations: Through advanced collaborative filtering techniques, our engine delivers highly personalized movie recommendations tailored to individual user preferences.


    Enhanced User Engagement: By accurately predicting user preferences, our recommendation engine has led to increased user engagement, resulting in longer session durations and higher user satisfaction.


    Scalable Performance: Leveraging the power of PyTorch and Apache Spark, our solution offers unparalleled scalability, efficiently processing vast amounts of data to deliver real-time recommendations without compromising performance.


    Measurable Impact: A/B testing and continuous monitoring have demonstrated tangible improvements in user interaction metrics, affirming the effectiveness of our recommendation engine in driving user engagement and satisfaction.


    The implementation of our recommendation engine has not only met but exceeded the project objectives, establishing a benchmark for personalized recommendation systems in the entertainment industry.

    Tools & Technologies used

    Let’s collaborate: