从声学分布中恢复音乐艺术家之间由专家评论家提供的网络邻接关系：一种构念效度方法
Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach
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- The University of Chicago（芝加哥大学）
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中文总结 AI 辅助
该研究提出从专家评论家的长篇散文中提取的评论邻接关系作为音乐推荐的新信号，通过声学分布建模实现冷启动下的边恢复，验证其构念效度，为MIR和MRS提供新方法。
中文摘要 AI 辅助
音乐推荐主要依赖两种信号：用户-物品交互信号，其在冷启动机制中失效；以及适用于任何录音的内在音乐内容。我们提出第三种尚未充分挖掘的信号，它更丰富且更具原则性：评论邻接关系，即当专家评论家在长篇散文中明确关联两位艺术家时建立的成对关系，它编码了关于哪些艺术家属于同一类别的刻意判断。先前的工作已确立其内部效度，表明它能恢复连贯、可解释的社区，且在无用户数据的用户满意度模拟中可与协同过滤相匹配。但缺失的是外部验证：这种由评论家提供的关系是否基于音乐本身而非社会学背景。我们将其与声学内容进行测试，将问题重新表述为构念效度问题。我们将艺术家表示为80个低级Essentia声学描述符的经验分布，并通过边际最优传输（Wasserstein）距离建模成对邻近度，在冷启动、艺术家不相交的划分下评估评论邻接关系在声学层面的可恢复程度。我们的集成模型在样本外AUC达到0.767（95%置信区间0.761-0.775），可恢复性随评论共识单调上升，在多源验证的边达到0.865。分层评估与流派的社会学模型一致：严格界定的、基于场景的流派比宽泛的行业总术语显示出更高的可恢复性。因此，评论话语是推荐的丰富信息源，可分解为可复制的“声学核心”和由叙事定位、亚文化背景及经典位置驱动的“社会学剩余部分”。该工作既提供了可扩展的冷启动发现机制，也为音乐信息检索（MIR）和音乐推荐系统（MRS）研究提供了基于社会学的方法。
英文摘要
Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start regime, and intrinsic musical content, available for any recording. We argue that a third, largely untapped signal is both richer and more principled: critical adjacency, the pairwise relation established when an expert critic explicitly links two artists in long-form prose. It encodes deliberate judgments about which artists belong together. Prior work established its internal validity, showing it recovers coherent, interpretable communities and can match collaborative filtering in user-satisfaction simulations, with no user data. What has been missing is external validation: whether this critic-sourced relation is grounded in the music itself versus sociological context. We test it against acoustic content, reframing the question as one of construct validity. Representing artists as empirical distributions over 80 low-level Essentia acoustic descriptors and modeling pairwise proximity via marginal optimal-transport (Wasserstein) distances, we evaluate how far critical adjacency is sonically recoverable under a cold-start, artist-disjoint split. Our ensemble recovers these edges at out-of-sample AUC of 0.767 (95% CI 0.761-0.775). Recoverability rises monotonically with critical consensus, reaching 0.865 on multi-source attested edges. Stratified evaluations align with sociological models of genre: tightly bounded, scene-based genres show higher recoverability than broad industry umbrella terms. Critical discourse is thus a rich source of information for recommendation, decomposing into a reproducible "sonic core" and a "sociological remainder" driven by narrative positioning, subcultural context, and canonical placement. The work offers both a scalable cold-start discovery mechanism and a sociologically grounded approach to MIR and MRS research.
