Consumer Preference Measurement of Folk Culture Based on Confidence Rule Base Model
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Abstract
Deep learning algorithms can help uncover patterns, make predictions, or generate new content related to folk culture, thus bridging the gap between heritage and advanced technology. The confidence model in a cloud context refers to a system or approach used to assess the reliability, security, or performance of cloud services. It might involve factors such as service uptime, data security, scalability, and compliance with industry standards. In this paper focused on the dynamic landscape of consumer preferences within folk music through cloud-based technologies integrated with deep learning. Folk music, with its rich cultural diversity and historical significance, presents a unique context for investigating the intricacies of consumer taste. The proposed model uses the "Ranking" based deep learning within cloud-based resources to predict and classify consumer preferences effectively. With the integration of the cloud confidence model ranking is implemented for the estimation of tracks in folk music. The estimated tracks are evaluated and stored in the cloud environment based on the preferences of the customers. The classification of the tracks and consumer preferences are ranked with the cloud model features. The simulation results demonstrated that the ranking of tracks effectively improves consumer preferences with the cloud confidence model in folk music. The results enhancing personalized experiences and facilitating informed decision-making for businesses and cultural institutions operating in the rich and diverse landscape of folk culture.
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