Analysis of Audience Preferences for Malaysian Chinese-Language Films with Chinese Cultural Themes Based on Data Mining and Recommender Systems
Keywords:
Recommendation System, Data Mining, User Key Concept Rate (UKCR), Malaysian Chinese-Language Films, Cultural ThemeAbstract
Recommendation System are designed to assist users in finding relevant items, products, services, or content that match their preferences and interests. Recommendation systems have gained widespread popularity in recent years due to the explosion of digital content and the need to help users navigate through the abundance of choices available. With the personalized content recommendation, this study presented a synergy between Data Mining and Recommender Systems to optimize film suggestions tailored to Audience Preferences for Malaysian Chinese-Language Films with Chinese Cultural Themes. Leveraging a unique approach that incorporates the Fuzzy Family Tree similarity algorithm and the User Key Concept Rate (UKCR) matrix, our research aims to enhance the accuracy and personalization of film recommendations. Through a comprehensive methodology involving user clustering, recommendation scores, and recommender ratings, we present a refined framework that augments the recommendation process. Simulation settings and performance metrics have enabled us to evaluate the system's efficacy, showcasing promising results. The findings reveal that our approach consistently aligns user preferences with film attributes, leading to an average recommendation accuracy of 86.5%. User clustering has facilitated the creation of distinct user segments, enhancing recommendation precision. The utilization of recommendation scores and recommender ratings has contributed to an average user satisfaction increase of 24.8% compared to traditional methods.
Downloads
References
Gao, C., Wang, X., He, X., & Li, Y. (2022, February). Graph neural networks for recommender system. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 1623-1625).
Guruge, D. B., Kadel, R., & Halder, S. J. (2021). The state of the art in methodologies of course recommender systems—a review of recent research. Data, 6(2), 18.
Al Fararni, K., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., & Sabri, A. (2021). Hybrid recommender system for tourism based on big data and AI: A conceptual framework. Big Data Mining and Analytics, 4(1), 47-55.
Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics, 11(1), 141.
Monti, D., Rizzo, G., & Morisio, M. (2021). A systematic literature review of multicriteria recommender systems. Artificial intelligence review, 54, 427-468.
Nitu, P., Coelho, J., & Madiraju, P. (2021). Improvising personalized travel recommendation system with recency effects. Big Data Mining and Analytics, 4(3), 139-154.
Wang, X., Zhang, R., Sun, Y., & Qi, J. (2021, March). Combating selection biases in recommender systems with a few unbiased ratings. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 427-435).
Huang, T., Dong, Y., Ding, M., Yang, Z., Feng, W., Wang, X., & Tang, J. (2021, August). Mixgcf: An improved training method for graph neural network-based recommender systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 665-674).
Zhou, C., Ma, J., Zhang, J., Zhou, J., & Yang, H. (2021, August). Contrastive learning for debiased candidate generation in large-scale recommender systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3985-3995).
Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 59.
Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., & He, X. (2021, August). Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1791-1800).
Gupta, V., Choudhary, D., Tang, P., Wei, X., Wang, X., Huang, Y., ... & Mahoney, M. W. (2021, August). Training recommender systems at scale: Communication-efficient model and data parallelism. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 2928-2936).
Zhang, Q., Lu, J., & Zhang, G. (2021). Recommender Systems in E-learning. Journal of Smart Environments and Green Computing, 1(2), 76-89.
Ali, S., Hafeez, Y., Humayun, M., Jamail, N. S. M., Aqib, M., & Nawaz, A. (2022). Enabling recommendation system architecture in virtualized environment for e-learning. Egyptian Informatics Journal, 23(1), 33-45.
Pande, S. M., Ramesh, P. K., ANMOL, A., Aishwarya, B. R., ROHILLA, K., & SHAURYA, K. (2021, April). Crop recommender system using machine learning approach. In 2021 5th international conference on computing methodologies and communication (ICCMC) (pp. 1066-1071). IEEE.
Zhang, J., & Dong, L. (2021). Image monitoring and management of hot tourism destination based on data mining technology in big data environment. Microprocessors and Microsystems, 80, 103515.
del Carmen Rodríguez-Hernández, M., & Ilarri, S. (2021). AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions. Knowledge-Based Systems, 215, 106740.
Rahayu, N. W., Ferdiana, R., & Kusumawardani, S. S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047.
Wang, S., Cao, L., Wang, Y., Sheng, Q. Z., Orgun, M. A., & Lian, D. (2021). A survey on session-based recommender systems. ACM Computing Surveys (CSUR), 54(7), 1-38.
Freire, M. N., & de Castro, L. N. (2021). e-Recruitment recommender systems: a systematic review. Knowledge and Information Systems, 63, 1-20.
Huang, L., Fu, M., Li, F., Qu, H., Liu, Y., & Chen, W. (2021). A deep reinforcement learning based long-term recommender system. Knowledge-Based Systems, 213, 106706.
Singh, P. K., Pramanik, P. K. D., Dey, A. K., & Choudhury, P. (2021). Recommender systems: an overview, research trends, and future directions. International Journal of Business and Systems Research, 15(1), 14-52.
Palomares, I., Porcel, C., Pizzato, L., Guy, I., & Herrera-Viedma, E. (2021). Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation. Information Fusion, 69, 103-127.
Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 6, 100114.
Saleem, Z., Alhudhaif, A., Qureshi, K. N., & Jeon, G. (2023). Context‐aware text classification system to improve the quality of text: A detailed investigation and techniques. Concurrency and Computation: Practice and Experience, 35(15), e6489.
Eminagaoglu, M. (2022). A new similarity measure for vector space models in text classification and information retrieval. Journal of Information Science, 48(4), 463-476.
Zhang, J., Chang, W. C., Yu, H. F., & Dhillon, I. (2021). Fast multi-resolution transformer fine-tuning for extreme multi-label text classification. Advances in Neural Information Processing Systems, 34, 7267-7280.
Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing, 98, 106935.
Nitu, P., Coelho, J., & Madiraju, P. (2021). Improvising personalized travel recommendation system with recency effects. Big Data Mining and Analytics, 4(3), 139-154.
Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7, 439-457.
Liang, D., & Yi, B. (2021). Two-stage three-way enhanced technique for ensemble learning in inclusive policy text classification. Information Sciences, 547, 271-288.
Ragesh, R., Sellamanickam, S., Iyer, A., Bairi, R., & Lingam, V. (2021, March). Hetegcn: heterogeneous graph convolutional networks for text classification. In Proceedings of the 14th ACM international conference on web search and data mining (pp. 860-868).
Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., & He, X. (2021, August). Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1791-1800).
Al Fararni, K., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., & Sabri, A. (2021). Hybrid recommender system for tourism based on big data and AI: A conceptual framework. Big Data Mining and Analytics, 4(1), 47-55.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
