# User Preference-Based Method for Characterizing Automotive Wheel Hub Styles
## 5 Discussion  
This study elucidates the interplay between user preferences, product style, and design elements by developing a conceptual model for automotive hubs. It converts the subtle and indistinct aspects of product style into tangible visual representations and quantifiable data. Reflecting on the research methodology and outcomes, the discussion of the research's innovations and shortcomings is as follows:  
1. To enhance design efficiency, this research proposes a VAE-DBSCAN clustering approach in the sample clustering phase, which demonstrates superiority in both clustering performance and time efficiency for wheel samples compared to methods like manual clustering and K-means. This improvement accelerates the process of wheel sample clustering and the extraction of design element features.
2. The study presents a dual-chain representation framework (AWSCM) capable of quantifying user preference weights (using an entropy-based semantic analysis) and the contribution of design elements (through Quantification Theory Type I), creating a transparent link between abstract styles and concrete design elements. This framework assists designers in comprehending the interconnections between user preferences, product style, and design elements, enabling them to rapidly produce designs that cater to user preferences.
3. The AWSCM proposed in this paper is a transparent-first approach that replaces the reliance on black-box algorithm recommendations and subjective experience with designer-centric symbolic logic and intuitive data, thereby enabling rapid and traceable design decision-making and enhancing design efficiency. Compared to research results based on text or physiological data (such as EEG), this paper adopts a symbolic representation for design elements. These symbolic elements are easier for both the general public and designers to recognize, understand, and utilize in decision-making. Compared to product design research based on intelligent algorithms, the model proposed in this paper is more transparent and is more likely to assist designers in understanding the correlation between design elements and styles. This understanding can contribute to the improvement of design thinking. However, in terms of design speed, this method may not be as fast as artificial intelligence algorithms.
4. The characterization results presented in this paper are based on a theoretical analysis with a limited number of samples. To enhance the universality and feasibility of these results, it is essential to increase the quantity of user evaluation data. This expansion will facilitate further validation and analysis of the model. Additionally,design involves the recombination of multiple elements. Future research will aim to explore and quantify the combined effects of different design elements on style perception. For instance, it will investigate whether the simultaneous presentation of design factor A51 and design factor A11 influences their individual contributions to the 'geometric' style.
5. When user preferences for an object are not singular but a composite of multiple intertwined styles, the design solution needs to balance different style requirements. The combination of style weights and quantitative analysis results (the product of style weights and bias correlation coefficients of design elements) reveals the quantitative relationship between each design item and style requirements. For a different number of styles, the quantitative results should be flexible. The style weights and analysis results can be recalculated based on the actual number of styles, and the combination can be recalculated accordingly.