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
Filters
Institutions
- UMA UKPAI SCHOOL OF THEOLOGY, UYO, AKWA IBOM STATE (AFFL TO UNIVERSITY OF UYO) 1
- Umaru Ali Shinkafi Polytechnic, Sokoto, Sokoto State 24
- Umaru Musa Yaradua University, Katsina, Katsina State 28
- Umca, Ilorin (Affiliated To University of Ibadan), Kwara State 1
- University of Abuja, Abuja, Fct 117
- University of Africa, Toru-Orua, Bayelsa State 4
- University of Benin, Benin City, Edo State 363
- University of Calabar Teaching Hospital School of Health Information Mgt. 1
- University of Calabar, Calabar, Cross River State 240
- University of Ibadan, Ibadan, Oyo State 14