AI-powered Digital Media Recommendation System | Businessware Technologies

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AI-powered Digital Media Recommendation System

AI-powered Digital Media Recommendation System
As more and more media content - like video games, videos, films and cartoons - is created, it gets harder for production companies and investors to decide which content is worth promoting and investing capital into. There is a strong incentive to heavily filter through the content from creators on the basis of potential commercial success. While potential success is difficult to measure and is often determined based on personal experience, it can be formalised to predict how popular the piece of media in question will be with consumers. By analysing different aspects of already popular media, one can learn what determines the success of said media with a high degree of confidence.
Date: Apr 2021

Overview

Our client is a production company which helps creators promote media content, like mobile and browser video games, attract more customers and generally increase its popularity. In order to give more accurate recommendations and determine which projects will be successful in the future, our client asked us to develop a system for analysis of digital media, like a video game recording. There are multiple features which could be analysed, like dominant colors, camera angle, how fast the camera moves, etc. Such a system would provide invaluable insight about media that is already successful, like which color pallete is the most pleasing to consumers, but also help give recommendations to developers on how their products can be improved to gain more success on the market.

The solution we have developed detects dominant colors present in a screen recording of a video game as well as detects which color harmony corresponds with the video the most. The system also detects camera speed and scene changes.

The application is currently used as part of a recommendation system for our client. It helped to analyse previous projects and detect what makes a successful media product and how new products can be improved. For us as a team this project was another interesting dive into computer vision.

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