Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
开发并验证了一个无监督机器学习模型，从Airbnb房源图片中自动提取视觉独特性，发现其与需求呈倒U型关系，且高回复率或高评分的房源从中获益更多。
Abstract Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years, featuring unique offerings from individual providers. However, scalable quantification of visual uniqueness and their impacts on platforms like Airbnb remain largely unexplored. We address this gap by developing, validating, and applying an unsupervised machine learning model to automatically extract uniqueness from images and quantify its impact on demand. We first construct a machine learning model, informed by cognitive psychology, to assess visual uniqueness in 481,747 property images, achieving high accuracy and interpretability. Next, we validate our model through three studies involving various participant populations and methods, confirming that the model’s predictions of visual uniqueness align with human judgment. Finally, we apply this model to demand data of Airbnb properties in New York City spanning 13 months. We find an inverted U-shaped relationship between visual uniqueness and demand, with two significant moderation effects: properties with higher response rates or overall ratings benefit more from visual uniqueness. This research provides valuable insights for P2P platforms like Airbnb, highlighting the strategic use of visual uniqueness to enhance visual appeal and market performance. It also offers a new methodological roadmap for integrating psychological insights into the development and validation of unsupervised machine learning models.