<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, architectural color is analyzed, and architectural color perception based on matrix decomposition is constructed. Then, a matrix decomposition-based color layout recommendation model for modern architecture is constructed by determining the user’s architectural color perception interest points. In order to better apply the model to interior design, a 3D interior scene is built and the color of the interior is optimized with the help of cross-sectional ring blending, and the model is combined with interior color design to build an interior intelligent recommendation interaction design algorithm model. And through the experimental analysis of color layout intelligent recommendation, it is concluded that the algorithm constructed in this paper improves by 10.8% in the N@10 dimension and 11.2% in the N@30 dimension. When data are scarce, the robustness is 0.091 for the number of interaction experiments 0-15 times in the R@10 dimension and 0.067 for the number of interaction experiments 30-35 times in the N@10 dimension. People are 17% more satisfied with its interior color layout, 22% more comfortable, 19% more surprised, 18% less disappointed, and 24% more infectious. Modern architectural color layouts based on the Internet context can optimize the interior color design.</p>
</abstract>

In this paper, architectural color is analyzed, and architectural color perception based on matrix decomposition is constructed. Then, a matrix decomposition-based color layout recommendation model for modern architecture is constructed by determining the user’s architectural color perception interest points. In order to better apply the model to interior design, a 3D interior scene is built and the color of the interior is optimized with the help of cross-sectional ring blending, and the model is combined with interior color design to build an interior intelligent recommendation interaction design algorithm model. And through the experimental analysis of color layout intelligent recommendation, it is concluded that the algorithm constructed in this paper improves by 10.8% in the N@10 dimension and 11.2% in the N@30 dimension. When data are scarce, the robustness is 0.091 for the number of interaction experiments 0-15 times in the R@10 dimension and 0.067 for the number of interaction experiments 30-35 times in the N@10 dimension. People are 17% more satisfied with its interior color layout, 22% more comfortable, 19% more surprised, 18% less disappointed, and 24% more infectious. Modern architectural color layouts based on the Internet context can optimize the interior color design.

Application of modern architectural color layout in interior design

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Application of modern architectural color layout in interior design"}

In this paper, architectural color is analyzed, and architectural color perception based on matrix decomposition is constructed. Then, a matrix...

Application of modern architectural color layout in interior design

Online veröffentlicht: 11. Okt. 2023

Seitenbereich: -

Eingereicht: 19. Nov. 2022

Akzeptiert: 21. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00589

Schlüsselwörter
Matrix decomposition, Intelligent recommendation, Perceived points of interest, Interior design

© 2023 Lingya Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of modern architectural color layout in interior design

Online veröffentlicht: 11. Okt. 2023

Seitenbereich: -

Eingereicht: 19. Nov. 2022

Akzeptiert: 21. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00589

SchlüsselwörterMatrix decomposition, Intelligent recommendation, Perceived points of interest, Interior design

© 2023 Lingya Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Matrix decomposition, Intelligent recommendation, Perceived points of interest, Interior design