FAKTORISASI MATRIKS MENGGUNAKAN STOCHASTIC GRADIENT DESCENT UNTUK OPTIMASI SISTEM REKOMENDASI HOTEL - tholib, abu; Maula, Inayatul; Hidayat, M. Noer (Computer Society of Nahdlatul Ulama (CSNU) Indonesia, 2024)
In today's digital world, recommendation systems have a very important role to help users find hotels that match their preferences. This research focuses on developing a hotel recommendation system by combining matrix factorization method with Stochastic Gradient Descent (SGD) algorithm. The matrix factorization method is used to model the hotel ranking data as the product of the user matrix and the hotel matrix. While for the Stochastic Gradient Descent (SGD) algorithm plays a role in optimizing model parameters efficiently, where the method will be tested on hotel rating datasets or ratings. Evaluation of model performance in this study, using metrics such as Root Mean Squared (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). This study shows fairly accurate results, with an RMSE value of 0.370312, an MSE value of 0.137131, and an MAE value of 0.089932. These results show that combining the matrix factorization method with Stochastic Gradient Descent (SGD) can be an effective solution for building a hotel recommendation system according to user preferences