Space design innovation driven by intelligent recommendation: the application and efficiency analysis of hybrid model

Abstract—As technology advances, virtual reality and artificial intelligence play an increasingly important role in interior design. The study proposes an intelligent space design method that optimizes the recommendation effect by combining the recommendation model. This method integrates an improved content recommendation algorithm and collaborative filtering algorithm, and uses a convolutional neural network to extract key features to improve computational efficiency. In the furniture design project, the research solved the problem of data sparseness through image similarity calculation and SlopOne algorithm. Experiments show that when the parameters are set to pcaNum=128 and simNum=4, the recommendation accuracy of the hybrid recommendation model reaches 70% and 88% at TopN=10 and TopN=30 respectively. In addition, when TopN increases to 20, the recommendation accuracy exceeds 80%. The research improves the accuracy of indoor model recommendation, which is of great significance for improving design efficiency and promoting the application of intelligent technology in the field of interior design.