Citation

Deng, Qianwei and Wong, Chee Onn and Sitharan, Roopesh (2024) Analysis of Audience Preferences for Malaysian Chinese-Language Films with Chinese Cultural Themes Based on Data Mining and Recommender Systems. International Journal of Intelligent Systems and Applications in Engineering, 12 (6s). pp. 415-431. ISSN 2147-6799

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Data mining, recommendation system
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Creative Multimedia (FCM)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Jan 2024 02:46
Last Modified:	31 Jan 2024 02:46
URII:	http://shdl.mmu.edu.my/id/eprint/12071

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