An Overview of the Math behind the working of Recommendation Systems

The way recommendation systems are made allows them to learn about the user and then match
the user with recommendations based on their preferences or areas of interest. The operation of
these recommendation systems involves numerous mathematical calculations in addition to the
gathering of user data. The recommendation system's mathematical models guide the execution of
these operations. In order to provide the user with appropriate recommendations, these models are
utilised to create prediction ratings, also known as prediction scores. In their operations, these
mathematical models take the mean average values into account. But there are drawbacks to using
the mean average numbers as well. In order to increase the accuracy of prediction ratings or
prediction scores and, consequently, the accuracy of the recommendation system, this study
suggests using a median-based strategy while performing these mathematical processes.