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Personalized Music Recommendation Algorithm Based on Spark Platform.

Juan Sun1

  • 1Department of Music, Handan University, Handan, Hebei 056005, China.

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Summary
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This study introduces a personalized music recommendation algorithm using the Spark platform and an improved artificial fish swarm algorithm (AFSA) for K-means clustering. The approach enhances accuracy and real-time performance for large music datasets.

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Traditional recommendation algorithms struggle with large-scale music data, exhibiting low accuracy and poor real-time performance.
  • Personalized music recommendations are crucial for user engagement in the digital music era.

Purpose of the Study:

  • To propose a novel personalized music recommendation algorithm leveraging the Spark platform.
  • To address the limitations of existing algorithms in terms of accuracy and real-time processing for extensive music datasets.

Main Methods:

  • Developed a recommendation algorithm integrating K-means clustering optimized by the artificial fish swarm algorithm (AFSA) on the Spark platform.
  • Applied collaborative filtering based on user-music scoring and attribute relationships to enhance recommendation accuracy.
  • Deployed and validated the model on the Yahoo Music and an online music platform dataset.

Main Results:

  • The improved AFSA effectively optimized K-means clustering centroids, yielding superior clustering results.
  • The Spark platform's distributed computing enhanced recommendation accuracy compared to traditional methods.
  • The proposed algorithm demonstrated significantly higher recommendation accuracy and real-time performance on large-scale music data.

Conclusions:

  • The hybrid approach combining AFSA-optimized K-means with Spark provides an effective solution for personalized music recommendation.
  • The algorithm meets the current demands for accurate and real-time music recommendations, especially for large datasets.
  • This methodology offers a scalable and efficient solution for the music recommendation domain.