Design aesthetics recommender system based on customer profile and wanted affect

TL;DR

This paper proposes a deep learning-based recommendation system that personalizes product design suggestions by capturing individual aesthetic preferences and desired emotional responses, specifically demonstrated with vase designs.

Contribution

It introduces a novel profiling approach based on consumer's preferred design and affect, integrating Kansei adjectives into a deep learning recommendation framework.

Findings

The system effectively predicts consumer preferences based on affective descriptors.

Personalized recommendations align with individual aesthetic and emotional preferences.

The approach enhances traditional recommendation systems by incorporating design aesthetics and emotional factors.

Abstract

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.