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Music Individualization Recommendation System Based on Big Data Analysis.

Pengfei Sun1

  • 1The School of Arts, Yangtze University, Jingzhou 434023, China.

Computational Intelligence and Neuroscience
|July 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel big data music recommendation system. It enhances music personalization by analyzing user behavior and music data, improving accuracy by 20%.

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Area of Science:

  • Computer Science
  • Data Science
  • Music Information Retrieval

Background:

  • Traditional music recommendation systems struggle with personalization.
  • Big data analysis offers potential for improved music recommendation.
  • Existing methods face challenges like cold start and data sparseness.

Purpose of the Study:

  • To propose a big data music individualization recommendation method.
  • To enhance collaborative filtering algorithms using user behavior.
  • To improve music similarity calculations for better recommendations.

Main Methods:

  • Integrated user behavior, context, information, and music data.
  • Improved collaborative filtering algorithm based on user behavior.
  • Calculated semantic similarity of lyrics and co-occurrence similarity of songs.
  • Utilized Hadoop distributed framework for processing.
  • Proposed a mixed similarity calculation formula combining music and label similarity.

Main Results:

  • The proposed big data music recommendation model achieved approximately 20% higher accuracy compared to collaborative filtering and hybrid models.
  • Demonstrated efficiency, scalability, and stability of the recommendation system.
  • Successfully alleviated cold start and data sparseness issues.

Conclusions:

  • The developed system effectively meets users' individual music needs.
  • The complementary relationship between different algorithms was identified and leveraged.
  • The proposed method represents a significant advancement in personalized music recommendation systems.