A Movie Recommendations: A Collaborative Filtering Approach Implemented in Python

Nor Syazana Abdul Kodit; Tajul Rosli Razak; Mohammad Hafiz Ismail; Shakirah Hashim; Tengku Zatul Hidayah Tengku Petra; Nur Farraliza Mansor

doi:10.24191/jcrinn.v9i1.428

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.

Downloads

Download data is not yet available.

References

Blank, D., Kumar, D., Meeden, L., & Yanco, H. (2003). Pyro: A Python-based versatile programming environment for teaching robotics. Journal on Educational Resources in Computing (JERIC), 3(4), 1-es.

Gogna, A., & Majumdar, A. (2015). A comprehensive recommender system model: Improving accuracy for warm and cold start users. IEEE Access, 3, 2803–2813.

Guo, B., Xu, S., Liu, D., Niu, L., Tan, F., & Zhang, Y. (2017). Collaborative filtering recommendation model with user similarity filling. 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), 1151–1154.

Husin, M. R. M., Razak, T. R., Malik, A. M. A., Nordin, S., & Abdul-Rahman, S. (2023). Hybrid collaborative movie recommendation system. In 2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 – Proceedings (pp. 274–280). https://doi.org/10.1109/AIDAS60501.2023.10284679

Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261–273.

Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12–32.

Özbal, G., Karaman, H., & Alpaslan, F. N. (2011). A content-boosted collaborative filtering approach for movie recommendation based on local and global similarity and missing data prediction. The Computer Journal, 54(9), 1535–1546.

Parthasarathy, G., & Sathiya Devi, S. (2023). Hybrid recommendation system based on collaborative and content-based filtering. Cybernetics and Systems, 54(4), 432–453.

Razak, T. R., Halim, I. H. A., Jamaludin, M. N. F., Ismail, M. H., & Fauzi, S. S. M. (2019). an exploratory study of hierarchical fuzzy systems approach in a recommendation system. Jurnal Intelek, 14(2), 174–186. https://doi.org/10.24191/JI.V14I2.233

Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). DNA extraction from plant leaves with Minilys. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval-SIGIR (Vol 2, pp. 253–260).

Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. Recommender Systems Handbook, 257–297.

Walek, B., & Fojtik, V. (2020). A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications, 158, 113452. https://doi.org/10.1016/J.ESWA.2020.113452

Wu, C.-S. M., Garg, D., & Bhandary, U. (2018). Movie recommendation system using collaborative filtering. IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) (pp. 11–15). IEEE Xplore. https://10.1109/ICSESS.2018.8663822

Yao, L., Sheng, Q. Z., Ngu, A. H. H., Yu, J., & Segev, A. (2014). Unified collaborative and content-based web service recommendation. IEEE Transactions on Services Computing, 8(3), 453–466.