The design recommendation phase predicts the customer's preference for engineering-optimized designs and recommends customized designs to the target audience. Design aesthetics and satisfaction are one of the most important factors influencing consumers' product choices, and customers' data must be learned to resolve design-performance conflicts and determine optimal design concepts.
Find optimal designs for individuals and for the market based on customer preference prediction
Learning design preferences through data
Personalized recommendation system for individuals
Finding the balance between performance and aesthetics
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