International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com N P PATNAIK M 3 4 (August - September) 2021 08 August-September enCollaborative filteringMovie recommendationPersonalizationUser similarityRecommender systems With the rapid growth of online streaming platforms providing personalized movie recommendations has become essential for enhancing user experience and engagement This study focuses on developing a movie recommendation system using a collaborative filtering approach The collaborative method relies on analyzing user preferences viewing histories and rating patterns to predict and suggest movies that align with a users interests By leveraging useritem interaction matrices the system identifies similarities among users or movies using techniques such as userbased and itembased collaborative filtering The study also explores the integration of matrix factorization and similarity algorithms to improve accuracy and scalability Experimental results demonstrate that the collaborative approach effectively captures user preferences and delivers relevant recommendations compared to traditional contentbased methods The proposed model contributes to improving recommendation accuracy user satisfaction and personalization in modern entertainment platforms Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2021 All Rights Reserved