  Design is a factor that plays an important role in consumer purchase
decisions. As the need for understanding and predicting various preferences for
each customer increases along with the importance of mass customization,
predicting individual design preferences has become a critical factor in
product development. However, current methods for predicting design preferences
have some limitations. Product design involves a vast amount of
high-dimensional information, and personal design preference is a complex and
heterogeneous area of emotion unique to each individual. To address these
challenges, we propose an approach that utilizes dimensionality reduction model
to transform design samples into low-dimensional feature vectors, enabling us
to extract the key representational features of each design. For preference
prediction models using feature vectors, by referring to the design preference
tendencies of others, we can predict the individual-level design preferences
more accurately. Our proposed framework overcomes the limitations of
traditional methods to determine design preferences, allowing us to accurately
identify design features and predict individual preferences for specific
products. Through this framework, we can improve the effectiveness of product
development and create personalized product recommendations that cater to the
unique needs of each consumer.
