Citation

Lai, Cheng-Yung and Hashim, Noramiza and Krisnawati, Lucia Dwi (2025) Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, and Future Trends. Journal of Informatics and Web Engineering, 4 (3). pp. 90-110. ISSN 2821-370X

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Abstract

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of each technique are listed and explained briefly. In addition, the actual application’s results are also presented in this paper. To evaluate the performance of the techniques, some of the important datasets that are used in evaluating recommender systems are also discussed along with measurement metrics to determine the effectiveness of technique. Example metrics used are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and so on. This paper synthesizes existing research to evaluate the advantages and limitations of diverse recommendation techniques, which aims to give suggestion on how to improve the design of movie recommendation systems so that the performance of the technique can be improved.

Item Type:	Article
Uncontrolled Keywords:	Content-Based Filtering, Collaborative Filtering, Hybrid-based, Machine Learning, Recommender System, Evaluation Metrics, Movie Recommender
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	10 Nov 2025 07:38
Last Modified:	10 Nov 2025 07:38
URII:	http://shdl.mmu.edu.my/id/eprint/14841

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