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
- University of Ilorin, Kwara State 402
- University of Jos, Jos, Plateau State 19
- University of Lagos 18
- University of Maiduguri ( - Elearning), Maiduguri, Borno State 3
- University of Maiduguri, Borno State 109
- University of Nigeria, Nsukka, Enugu State 270
- University of Port Harcourt Teaching Hospital, Port Harcourt , River State 6
- University of Port-Harcourt, Rivers State 177
- University of Uyo, Akwa Ibom State 209
- Usmanu Danfodio University, Sokoto, Sokoto State 245