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
- Landmark University, Omu-Aran, Kwara State 1
- Lead City University, Ibadan, Oyo State 1
- Lens Polytechnic, offa, Kwara State. 215
- Madonna University, Elele, Rivers State 20
- Madonna University, Okija, Anambra State 2
- Mcpherson University, Seriki Sotayo, Ogun State 1
- Michael and Cecilia Ibru University, Owhrode, Delta State 1
- Michael Okpara University of Agriculture, Umudike 43
- Michael Otedola Col of Primary Educ. Epe, Lagos (affl To University of Ibadan) 8
- Modibbo Adama University, Yola, Adamawa State 15