Nowadays, many people appetite to watch TV-shows or - series anytime and anywhere they want. In recent years, online TV has experienced exponential growth. Netflix is one of the parties that jumped into the world of online streaming services. In this effort, many subsist movie recommendation approaches learn a user ranking model from user feedback with respect to the movie’s content. Unfortunately, this approach suffers from the sparsity problem inherent in SMR data. Collaborative filtering (CF) is the workhorse of recommender engines since it can perform feature learning on its own, meaning it learns for itself what features to use. CF can be split into Memory-Based Collaborative Filtering and Model-Based Collaborative filtering. Here compare results from memory-based CF, model-based CF and third approach which uses an algorithm called `Stochastic gradient descent` for collaborative filtering. The propose stochastic gradient descent algorithm using movie recommender system. In this propose system use movie lens dataset, one of the most common datasets used to implement and test recommender engines. It contains 100,000 movie ratings from 943 users and a selection of 1682 movies. Evaluate the results using the Root Mean Squared Error (RMSE) and Mean Absolute Error(MAE).

Movie Recommendation System, Memory-Based Collaborative Filtering, Model-Based Collaborative Filtering, Stochastic Gradient Descent

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