FILM RECOMMENDATION SYSTEM WITH MATRIX FACTORIZATION METHOD BASED ON COLLABORATIVE FILTERING

• المؤلف الرئيسي: Aji Permadi, Akhmad
• التنسيق: Final Project
• اللغة: Indonesia
• الوصول للمادة أونلاين: https://digilib.itb.ac.id/gdl/view/79248
• المؤسسة: Institut Teknologi Bandung

The rapid development of the film industry has made film recommendation systems increasingly popular. A film recommendation system is a system that provides a list of film recommendations to users. There are several types of recommendation systems, including knowledge-based recommenders, content-based systems, and collaborative filtering. In this Final Project, the collaborative filtering type is used in the matrix factorization method. Matrix factorization is a method that factors the user-item matrix into two smaller matrices, namely the user matrix and the item matrix. This Final Project aims to create and carry out an analysis of the impact of hyperparameters on the film recommendation system model using the matrix factorization method. The dataset used in this final assignment is MovieLens 100k which contains a total of one hundred thousand ratings given by 943 users in 1682 movies. Model training is carried out by varying hyperparameters, namely: number of features (k), learning rate (?), and regularization factor (?). The randomness initiation matrix is divided into three initiation matrices. The hyperparameter value of the number of features (k) determines the level of complexity of the model, increasing the value of k will improve the model’s performance in providing predictions. The number of iterations required by a model to achieve a certain performance can be influenced by the learning rate (?). Choosing a value of ? that is too large causes the model to not converge. The regularization factor (?) determines how much penalty is applied to the model so that it can help the model avoid over-fit. Differences in the initiation of the P and Q matrices in the training process can affect the resulting model, namely in providing a different list of recommendations.