This document discusses building recommendation systems for video on demand platforms. It introduces three algorithms used: co-occurrence collaborative filtering, which recommends items frequently viewed together; collaborative filtering, which predicts ratings; and binary logistic regression, which improves accuracy with more user data. Co-occurrence performs best on dynamic data but accuracy does not improve. Collaborative filtering uses ratings but cannot add movie/user data. Regression has highest accuracy long-term but slow training. An event generator and ensemble of models are also discussed. The recommendation system is part of a larger video platform project.