Author(s)

Haiyi Lv ¹

Affiliation(s)

¹ Jilin University of Arts, Changchun City, China

Corresponding Author

Haiyi Lv

ABSTRACT

With the rapid development of digital music platforms, the dissemination of Chinese folk music faces challenges such as insufficient user coverage, uneven distribution of cultural values, and limited effectiveness of personalized recommendations. To address this issue, this paper proposes a method for optimizing the dissemination path of folk music based on an intelligent recommendation algorithm. This method combines collaborative filtering, cultural feature weighting, and a social network diffusion model to achieve a multi-dimensional integration of user interests, regional culture, and musical characteristics. This study constructs vectors of user behavior and cultural labels, generating recommendation scores through weighted fusion. It also uses interest clustering and independent cascade models to simulate the diffusion path and dynamically adjusts recommendation weights based on the diffusion effect. Experimental results show that the average score for each survey dimension is above 4, indicating that users generally approve of the system's recommendation effectiveness. The diversity score for the recommended results is 4.2 (standard deviation 0.6), demonstrating that the system is able to provide diverse ethnic and folk music content while maintaining user interest matching, avoiding single-minded recommendations.

KEYWORDS

Chinese Folk Music Dissemination; Intelligent Recommendation Algorithm; Collaborative Filtering; Interest Clustering; Dissemination Path Optimization

CITE THIS PAPER

Haiyi Lv, Optimizing the Dissemination Path of Chinese Folk Music Based on an Intelligent Recommendation Algorithm. Media and Communication Research (2025) Vol. 6: 143-151. DOI: http://dx.doi.org/10.23977/mediacr.2025.060417.

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