Due to the poor effectiveness of knowledge recommendations in existing service models,this study explores a library knowledge service model that integrates user wisdom.The smart library employs digital twin technology to demonstrate intelligent interaction between virtual and real environments,achieving personalized two-way interactive services.Based on the highest confidence results,it uses methods such as scene reconstruction to build a more com-prehensive service environment for the smart library.The RT algorithm is used to predict the rating matrix,and recom-mendations are made according to the scene model that integrates user implicit feedback,promoting interaction and connection between users and the platform.Experimental results show that the distribution of TPR-FPR tends towards the top-left corner,indicating that the knowledge recommendation effect under this model is relatively ideal,with out-comes meeting expectations.This demonstrates that users play a central role in the process of knowledge service.
关键词
用户智慧/图书馆/知识服务/置信度/RT算法/虚拟现实交互
Key words
user wisdom/library/knowledge service/confidence level/RT algorithm/virtual reality interaction
万方数据