Enhancing Music Recommendation Systems Through Emotion Recognition and User Behavior Analysis
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초록
요약 배경: 기존 음악 추천 시스템은 가사의 의도된 감정과 사용자가 실제로 느끼는 감정 사이의 불일치를 충분히 고려하지 않았다. 모델: 본 연구에서는 LDA 모델을 활용하여 가사와 사용자댓글의 주제 벡터를 생성하고, 시간 감쇠 효과와 재생 횟수를 반영한 사용자 행동 궤적과 통계특성을 결합하여 사용자 선호도 모델을 구축했다. 결과: 실증 분석 결과, 제안 모델이 가사만 활용한 기존 모델보다 높은 정확도로 음악을 추천했다. 시사점: 본 연구는 감정 인식과 사용자 행동분석을 통합하여 개인화된 음악 추천 시스템을 개선하는 새로운 방법론을 제시한다.
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.
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