Personalized Recommendation Algorithm of Interior Design Style Based on Local Social Network
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초록
To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimizedrecommendation algorithm for interior design style based on local social network, which includes dataacquisition 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 thelocation-based social network of association rule mining algorithm, the association analysis of the 3D modeldataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimentalresults show that the proposed algorithm can complete effective analysis of 3D interior home style withthe recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellentapplication effect.
Abstract
To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimizedrecommendation algorithm for interior design style based on local social network, which includes dataacquisition 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 thelocation-based social network of association rule mining algorithm, the association analysis of the 3D modeldataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimentalresults show that the proposed algorithm can complete effective analysis of 3D interior home style withthe recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellentapplication effect.
참고문헌 (22)
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