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In a context where the entertainment industry is swiftly shifting towards streaming platforms and on-demand consumption, audiences now enjoy immediate access to an unlimited variety of content on their own terms, challenging the traditional paradigm of television programming.
The offering of audiovisual content is substantial, and the number of available options can be overwhelming; users find themselves facing a constant challenge: how to navigate and select from the abundant array of movies, series, and programs available on streaming services. With such a broad diversity of genres, platforms, and themes, making a decision about what to watch can become a time-consuming and energy-draining task. Often, users spend more time searching for something to watch than actually enjoying the content itself. Furthermore, personal preferences, mood, and changing user circumstances can influence what they want to watch at any given moment. VER is a content recommendation engine for streaming (Netflix, Amazon Prime Video, HBO, Disney+), which assists users in dealing with the exhausting task of choosing content.
To facilitate the user not only in accessing all the available content in a unified catalog but also in obtaining personalized recommendations quickly and easily, tailored to their preferences and circumstances at each moment.
Foqum has deployed a recommendation algorithm, which is a key component of the service, using a combination of BIG DATA (content scores obtained from The Movie Database and preferences of similar users) and SMALL DATA (user's own preferences within the application). It offers entirely personalized recommendations with an accuracy rate exceeding 90%. The goal is to ensure that VER users always receive tailored recommendations aligned with their tastes, maximizing their streaming service experience.
Remember: we cannot improve what we do not measure.
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