Playing music is considered essential for some businesses. When entering a clothing store, a café or a gym, there is most often some music playing in the background. The employees do not have the ability to select music optimally to maximize profit. Their expertise lies within their main duties of the workplace and they should spend most of their time focusing on those duties for an efficient workflow. The problem that arises is how businesses can play suitable music with minimal effort in music selection. To solve this, a recommender system is built with the real-time machine learning algorithm, DR-TRON. It is a lightweight and dynamic algorithm that instantly improves on user interaction. By using the dynamic nature of the algorithm, a more trivial model was initially built to test for some valuable output. Afterward, a more complex model was built where there was more consideration in music channel properties. The second model recommends suitable music channels and reduces the effort of selection.