用内容分析补足听歌少的推荐短板，提升音乐推荐准确率
Content filtering methods for music recommendation: A review
- 结合歌词与音频特征进行内容分类，弥补用户行为数据稀疏
- 利用大语言模型和信号处理技术实现多模态歌曲分析
- 适合做音乐推荐系统优化的研究者与工程师参考
推荐系统在现代音乐流媒体平台中至关重要，影响用户发现和互动歌曲的方式。协同过滤通过相似用户的偏好推荐内容，但在交互稀疏的媒介中效果不佳。音乐即为典型例子，用户通常只听过极少数曲目。为此，本文综述了当前研究进展，重点探讨内容过滤如何缓解协同过滤固有的偏差。文章分析了基于大语言模型的歌词分析与音频信号处理等歌曲分类方法，并讨论不同分析手段间的潜在冲突，提出解决路径。
原文摘要 · Abstract (English)
Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests content based on the preferences of users with similar listening patterns to the target user. However, this method is less effective on media where interactions are sparse. Music is one such medium, since the average user of a music streaming service will never listen to the vast majority of tracks. Due to this sparsity, there are several challenges that have to be addressed with other methods. This review examines the current state of research in addressing these challenges, with an emphasis on the role of content filtering in mitigating biases inherent in collaborative filtering approaches. We explore various methods of song classification for content filtering, including lyrical analysis using Large Language Models (LLMs) and audio signal processing techniques. Additionally, we discuss the potential conflicts between these different analysis methods and propose avenues for resolving such discrepancies.
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