Framework of personalized layout for a museum exhibition hall

In order to generate a highly personalized, interactive and time-independent layout of artifacts in museums, to solve the limitations of real-world museums in terms of places, layouts and exhibition forms, to enrich the functions of existing digital museums, and to use the layout in digital museums to better guide the layout in real museums, a framework for personalized layout of museum exhibition halls is proposed. The framework consists of five modules: information preparation, personalized recommendation, random placement, optimization, and user interaction. The personalized recommendation module uses neural networks to train personalized recommendation models; the random placement module includes the layout of exhibit cases in the exhibition hall and the random filling of exhibit cases with artifacts, and proposes the Placement depends on Integrated path (PIP) algorithm. The optimization module includes efficiency optimization and rendering optimization; the user interaction module includes personal information collection, roaming, map navigation, and exhibit interaction functions. The experimental results show that the layout generated by this framework has a high degree of realism, scene loading speed and smoothness of online viewing, and the efficiency of artifact screening is improved to 7 times compared with that before optimization; the user tuning results show that more than 85% of people give realistic or very realistic evaluation to the layout, and the framework and algorithm in the paper can realize the personalized recommendation of artifacts and exhibition hall layout in a realistic, effective, real-time and interactive way.