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A Data-Driven Hybrid Movie Recommendation System for Personalized Suggestions
Subject area: Science,Engineering and Technology · Area of research: Machine Learning and Data Science
Abstract
The front end of the Movie Recommendation System is developed using Streamlit, providing an interactive and user-friendly interface for users to search and explore movies efficiently. The interface is enhanced with custom CSS styling to deliver a visually appealing and responsive experience through modern design elements such as animated cards and dynamic layouts. The back end is implemented using Python and integrates machine learning techniques to generate accurate recommendations. The system employs TF-IDF vectorization and cosine similarity for content-based filtering, allowing it to analyze movie features such as genres, keywords, cast, and overview. Additionally, collaborative filtering is implemented using Truncated Singular Value Decomposition (SVD) to capture user preferences and latent patterns from rating data. A hybrid approach is used by combining both methods to improve recommendation accuracy and personalization. The system utilizes datasets processed with Pandas and NumPy for efficient data handling. Furthermore, it integrates the OMDb API to fetch real-time movie details such as posters, ratings, and plot descriptions, enhancing user engagement. Streamlit session state and caching mechanisms are used to optimize performance and maintain seamless user interaction. This solution provides an efficient and scalable approach for personalized movie recommendations. In the future, the system can be extended with advanced machine learning models, user authentication, and deployment as a full-scale web or mobile application to support real-world usage.
Keywords
Hybrid Recommendation System, Machine Learning Algorithms, TF-IDF Feature Extraction, Cosine Similarity Analysis, Latent Feature Extraction, Singular Value Decomposition, Personalized Recommendation Engine, Streamlit-Based Interface, API Integration, Data-Driven Systems.
References
[1] A. Kumar and S. Ramesh, “Design and Implementation of Scalable Movie Recommendation Systems Using Machine Learning,” International Journal of Data Science and Analytics, vol. 12, no. 3, pp. 45–58, 2023.
[2] J. Smith, E. Johnson, and M. Davis, “Hybrid Recommendation Techniques for Personalized Content Delivery,” Journal of Artificial Intelligence Research, vol. 9, no. 2, pp. 110–125, 2022.
[3] P. Sharma and R. Gupta, “Application of Collaborative Filtering in Movie Recommendation Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 1, pp. 78–90, 2024.
[4] L. Chen and H. Wei, “Content-Based Filtering Using TF-IDF and Cosine Similarity for Recommendation Systems,” Journal of Information Retrieval Systems, vol. 8, no. 3, pp. 200–214, 2021.
[5] M. Rodriguez and T. Patel, “Enhancing Recommendation Accuracy Using Hybrid Machine Learning Models,” International Conference on Machine Learning Applications, pp. 34–41, 2023.
[6] K. O’Connor and B. Silva, “Efficient Data Processing Techniques for Large-Scale Recommendation Systems,” Data Engineering Review, vol. 14, no. 2, pp. 88–102, 2023.
[7] F. Hassan and Y. Kim, “Dimensionality Reduction Using Singular Value Decomposition for Recommendation Systems,” Journal of Computational Intelligence, vol. 6, no. 1, pp. 11–25, 2022.
[8] E. Rossi and C. Bianchi, “User Experience Enhancement in Interactive Data Applications Using Streamlit,” Journal of Modern Web Applications, vol. 11, no. 3, pp. 300–315, 2024.
[9] D. Mitchell, “API Integration for Real-Time Data Retrieval in Web-Based Applications,” Transactions on Web Engineering, vol. 5, no. 4, pp. 250–264, 2023.
[10] S. Lee and J. Park, “Personalized Recommendation Systems for Digital Media Platforms,” Journal of Intelligent Systems and Applications, vol. 7, no. 2, pp. 55–67, 2022.
How to cite this paper
@article{1715724,
author = {S. Rajagopalan, Dr. K. Ponmozhi},
title = {A Data-Driven Hybrid Movie Recommendation System for Personalized Suggestions},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {3070-3077},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715724.pdf},
abstract = {The front end of the Movie Recommendation System is developed using Streamlit, providing an interactive and user-friendly interface for users to search and explore movies efficiently. The interface is enhanced with custom CSS styling to deliver a visually appealing and responsive experience through modern design elements such as animated cards and dynamic layouts. The back end is implemented using Python and integrates machine learning techniques to generate accurate recommendations. The system employs TF-IDF vectorization and cosine similarity for content-based filtering, allowing it to analyze movie features such as genres, keywords, cast, and overview. Additionally, collaborative filtering is implemented using Truncated Singular Value Decomposition (SVD) to capture user preferences and latent patterns from rating data. A hybrid approach is used by combining both methods to improve recommendation accuracy and personalization. The system utilizes datasets processed with Pandas and NumPy for efficient data handling. Furthermore, it integrates the OMDb API to fetch real-time movie details such as posters, ratings, and plot descriptions, enhancing user engagement. Streamlit session state and caching mechanisms are used to optimize performance and maintain seamless user interaction. This solution provides an efficient and scalable approach for personalized movie recommendations. In the future, the system can be extended with advanced machine learning models, user authentication, and deployment as a full-scale web or mobile application to support real-world usage.},
keywords = {Hybrid Recommendation System, Machine Learning Algorithms, TF-IDF Feature Extraction, Cosine Similarity Analysis, Latent Feature Extraction, Singular Value Decomposition, Personalized Recommendation Engine, Streamlit-Based Interface, API Integration, Data-Driven Systems.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715724}
}