Abstract: This paper presents Movie recommendation and advanced with the exponential growth of digital content, recommender systems have become essential tools for improving user experience and driving engagement. This paper presents the design and implementation of a movie recommendation system using Python. We explore collaborative filtering, content-based filtering, and hybrid approaches. The system is built using publicly available datasets such as MovieLens and employs tools such as Pandas, Scikit-learn, and Surprise. Our results show that hybrid recommendation systems yield better performance and personalization compared to single-method models.
Keywords: Movie recommendation, collaborative filtering, content-based filtering, hybrid system, Python, machine learning.
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DOI:
10.17148/IJARCCE.2025.145107
