We present a method to determine Fashion DNA, coordinate vectors locating fashion items in an abstract space. Our approach is based on a deep neural network architecture that ingests curated article information such as tags and images, and is trained to predict sales for a large set of frequent customers. In the process, a dual space of customer style preferences naturally arises. Interpretation of the metric of these spaces is straightforward: The product of Fashion DNA and customer style vectors yields the forecast purchase likelihood for the customer-item pair, while the angle between Fashion DNA vectors is a measure of item similarity. Importantly, our models are able to generate unbiased purchase probabilities for fashion items based solely on article information, even in absence of sales data, thus circumventing the "cold-start problem" of collaborative recommendation approaches. Likewise, it generalizes easily and reliably to customers outside the training set. We experiment with Fashion DNA models based on visual and/or tag item data, evaluate their recommendation power, and discuss the resulting article similarities.

本研究提出了一种基于深度神经网络的“时尚DNA”方法，通过标签和图像等信息预测大量常见顾客的购买行为，同时生成客户风格偏好的双重空间，利用时尚基因向量和客户风格向量的乘积预测购买概率，基于文章信息生成无偏购买概率，推荐力度强，适用于各种顾客。

时尚DNA: 结合内容和销售数据用于推荐和文章映射