Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect.

翻译：自电子商务发展初期，商品推荐系统便发挥着关键作用。借助大数据与先进深度学习方法的推动，其发展进一步深化，其中消费者画像技术居核心地位。如今，基于消费者过往个人选择及相似群体的选择，已能预测其兴趣偏好。然而，当前定义的"选择"仍局限于可量化的数据维度，如产品特征、价格与类型。本文探索了基于消费者偏好的产品设计与预期情感进行画像的可行性。我们以花瓶设计为案例，对每款设计进行个体化感性研究。基于文献综述中关于消费者对产品设计反应的结论，确定了本研究关注的消费者个体特征。我们构建了具有代表性的消费者模型，该模型采用深度学习方法形成推荐系统的核心。系统通过感性形容词引导新消费者描述其期望情感，并据此推荐最可能引发该情感的美学设计。