Movie Recommendation System Using User-Based Collaborative Filtering (ubcf)
Student: Abdulmalik Kaka Musa (Project, 2025)
Department of Computer Information and Communication Science
Federal University, Dutsin-Ma, Katsina State
Abstract
This project presents the development of a movie recommendation system using User-Based Collaborative Filtering (UBCF). The system is designed to alleviate the problem of information overload and enhance user satisfaction on movie streaming platforms. Using the MovieLens 100K dataset, the system identifies user preferences through rating patterns and recommends movies based on similarities computed with Pearson Correlation. The project was implemented in Python using libraries such as Pandas and Scikit-learn. Evaluation metrics such as Precision (0.82), Recall (0.75), and F1-Score (0.78) demonstrate the effectiveness of the system. Though limited by cold-start and sparsity issues, the results show promising potential for lightweight recommendation systems in real-world applications.
Keywords
For the full publication, please contact the author directly at: abdulmalikmusakaka@gmail.com
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Institutions
- Binyaminu Usman Polytechnic, Hadijia, Jigawa State 3
- Borno State University, Maiduguri, Borno State 15
- Bowen University, Iwo, Osun State 1
- Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State 255
- College of Agriculture and Animal Science, Mando Road, Kaduna, Kaduna State 1
- College of Agriculture, Science and Technology, Lafia, Nasarawa State 8
- College of Education, Akwanga (affl To Ahmadu Bello Univ, Zaria) 1
- College of Education, Eha Amufu, (Affliliated To Unn), Enugu State 1
- College of Education, Warri (Affiliated To Delta State Uni, Abraka), Delta State 1
- College of Health Technology, Calabar, Cross River State 1