A Movie Recommendations: A Collaborative Filtering Approach Implemented in Python

A Movie Recommendations: A Collaborative Filtering Approach Implemented in Python

Authors

  • Nor Syazana Abdul Kodit School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Arau Campus, Arau, Perlis.
  • Tajul Rosli Razak School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor.
  • Mohammad Hafiz Ismail School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Arau Campus, Arau, Perlis.
  • Shakirah Hashim School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor.
  • Tengku Zatul Hidayah Tengku Petra School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor.
  • Nur Farraliza Mansor School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor.

DOI:

https://doi.org/10.24191/jcrinn.v9i1.428

Keywords:

Collaborative Filtering, Recommendation System, Movie Selection, Cosine Similarity

Abstract

In-home entertainment, selecting the perfect movie is a pervasive challenge, amplified by many streaming platforms like Netflix and Amazon. This study introduces a groundbreaking Movie Recommendation System with Collaborative Filtering (MRS-CF), meticulously implemented in Python. Employing Item-Based Collaborative Filtering with Cosine Similarity, the system assesses inter-movie relationships based on user-submitted titles, explicitly focusing on genre distinctions. The core contribution of MRS-CF lies in its ability to expedite the movie selection process, swiftly presenting users with a curated list of ten recommended movies strategically organised by descending similarity. Augmented with individual similarity scores, this system is crafted to optimise the user’s movie-watching experience. Thirty participants were evaluated through the Perceived Ease of Use (PEOU). The PEOU results underscore the profound contribution of MRS-CF, revealing elevated user satisfaction across all dimensions. This research illuminates the potent impact of the MRS-CF, emphasising its role as a transformative tool for refining and enhancing personalised movie recommendations.

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Published

2024-03-01

How to Cite

Abdul Kodit, N. S., Razak, T. R., Ismail, M. H., Hashim, S., Tengku Petra, T. Z. H., & Mansor, N. F. (2024). A Movie Recommendations: A Collaborative Filtering Approach Implemented in Python. Journal of Computing Research and Innovation, 9(1), 257–268. https://doi.org/10.24191/jcrinn.v9i1.428

Issue

Section

General Computing

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