4 research outputs found
Design aesthetics recommender system based on customer profile and wanted affect
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 the 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
Design aesthetics recommender system based on customer profile and wanted affect
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.Comment: 11 pages, 10 figures, peer-reviewed at the KEER 2022 conferenc
KEER2022
AvanttĂtol: KEER2022. DiversitiesDescripciĂł del recurs: 25 juliol 202