  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.
