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Enhancing Music Recommendation Systems Through Emotion Recognition and User Behavior Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(5), pp.177-187
  • DOI : 10.9708/jksci.2024.29.05.177
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 2, 2024
  • Accepted : April 1, 2024
  • Published : May 31, 2024

Qi Zhang 1

1경기대학교

Accredited

ABSTRACT

177-Existing music recommendation systems do not sufficiently consider the discrepancy between the intended emotions conveyed by song lyrics and the actual emotions felt by users. In this study, we generate topic vectors for lyrics and user comments using the LDA model, and construct a user preference model by combining user behavior trajectories reflecting time decay effects and playback frequency, along with statistical characteristics. Empirical analysis shows that our proposed model recommends music with higher accuracy compared to existing models that rely solely on lyrics. This research presents a novel methodology for improving personalized music recommendation systems by integrating emotion recognition and user behavior analysis.

Citation status

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