The success of creative products depends upon the felt experience of consumers. Capturing such consumer reactions requires the fusing of different types of experiential covariates and perceptual data in an integrated modeling framework. In this paper, the authors develop a novel multimodal machine learning framework that combines multimedia data (e.g., metadata, acoustic features and user generated textual data) in creative product settings and apply it for predicting the success of musical albums and playlists. The authors estimate the proposed model on a unique dataset which they collected using different online sources. The model integrates different types of nonparametrics to flexibly accommodate diverse types of effects. It uses penalized splines to capture the nonlinear impact of acoustic features and a supervised hierarchical Dirichlet process to represent crowd-sourced textual tags. It captures dynamics via a state-space specification. The authors show the predictive superiority of the model with respect to several benchmarks. The results illuminate the dynamics of musical success over the past five decades. The authors then use the components of the model for marketing decisions such as forecasting the success of new albums, album tuning and diagnostics, construction of playlists for different generations of music listeners, and contextual recommendations.

创意产品的成功取决于消费者的感受。捕捉此类消费者反应需要在集成的建模框架中融合不同类型的经验协变量和感知数据。在本文中，作者开发了一种新颖的多模式机器学习框架，该框架在创意产品设置中结合了多媒体数据（例如，元数据，声学特征和用户生成的文本数据），并将其用于预测音乐专辑和播放列表的成功。作者在一个独特的数据集上估计了该模型，他们使用不同的在线资源收集了该数据集。该模型集成了不同类型的非参数，以灵活地适应各种类型的效果。它使用受罚样条来捕获声学特征的非线性影响，并使用监督的层次化Dirichlet流程来表示众包文本标签。它通过状态空间规范捕获动态。作者展示了该模型相对于几个基准的预测优越性。结果阐明了过去五年来音乐成就的动力。然后，作者将模型的组件用于市场营销决策，例如预测新专辑的成功，专辑的调优和诊断，为不同代音乐听众构建播放列表以及上下文推荐。结果阐明了过去五年来音乐成就的动力。然后，作者将模型的组件用于市场营销决策，例如预测新专辑的成功，专辑的调优和诊断，为不同代音乐听众构建播放列表以及上下文推荐。结果阐明了过去五年来音乐成就的动力。然后，作者将模型的组件用于市场营销决策，例如预测新专辑的成功，专辑的调优和诊断，为不同代音乐听众构建播放列表以及上下文推荐。