Sort Filter Skyline in Movie Recommendation Based on Individual Preferences: Performance and Time Complexity Analysis - Fadhilah, Alvi Nur; Cahyanto, Triawan Adi; Saifudin, Ilham (Universitas Negeri Semarang, 2024)
Purpose: This study seeks to deliver accurate, customized movie recommendations using the Sort Filter Skyline (SFS) algorithm. The approach considers factors like budget, box office earnings, popularity, runtime, and audience ratings to align closely with each user's specific preferences. Methods: The Sort Filter Skyline (SFS) algorithm is employed, designed to identify and recommend items different from others within the dataset. Initially, the data undergoes preparation through pre-processing before being analyzed to compute entropy using the entropy formula. Before carrying out the dominance test, the SFS algorithm organizes the data based on entropy values. Result: In this research, 176 skyline objects were identified from a dataset containing 4,803 movies, including well-known titles like