Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Personalized Recommendation Algorithm of Interior Design Style Based on Local Social Network

  • Guohui Fan (Dept. of Architectural Engineering, Henan Polytechnic Institute) ;
  • Chen Guo (Dept. of Architectural Engineering, Henan Polytechnic Institute)
  • Received : 2022.04.05
  • Accepted : 2022.08.20
  • Published : 2023.10.31
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Abstract

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.

Keywords

Acknowledgement

This study was supported by the Collaborative Innovation Mode of the Integration of Industry and Education under the National Strategy of Civil-Military Integration (No. 212400410445) and Henan Provincial Key Research and Promotion Project (Soft Science Research) in 2021.

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