Library, Shanghai University of Engineering Science, Shanghai 201620, China.
Assembly Department, Shanghai Aerospace Equipments Manufacturer Co., Ltd., Shanghai 200245, China.
Comput Intell Neurosci. 2022 Aug 27;2022:3582719. doi: 10.1155/2022/3582719. eCollection 2022.
In order to improve the Resource Recommendation and sharing ability of mobile library, an intelligent optimization model of Mobile Library Resource Recommendation Service Based on digital twin technology is proposed. Build the association rule feature distribution set of mobile library resource recommendation service, carry out text information retrieval in the process of Mobile Library Resource Recommendation and sharing, carry out semantic correlation feature registration according to the retrieval preference of mobile library reading user object, establish the association rule data set of mobile library reading user object preference for mobile library Resource Recommendation and sharing, carry out feature block processing, and analyze the library reader preference. Complete the collaborative filtering recommendation of Mobile Library Resource Recommendation sharing. The simulation results show that the collaborative recommendation under the intelligent optimization mode of mobile library resource recommendation service using this method has high accuracy and good confidence level, which improves the intelligent level of Mobile Library Resource Recommendation and user satisfaction.
为提高移动图书馆资源推荐和共享能力,提出一种基于数字孪生技术的移动图书馆资源推荐服务智能优化模型。构建移动图书馆资源推荐服务的关联规则特征分布集,在移动图书馆资源推荐和共享过程中进行文本信息检索,根据移动图书馆阅读用户对象的检索偏好进行语义相关特征注册,建立移动图书馆阅读用户对象偏好的关联规则数据集,进行特征块处理,并分析图书馆读者偏好。完成移动图书馆资源推荐共享的协同过滤推荐。仿真结果表明,采用该方法的移动图书馆资源推荐服务智能优化模式下的协同推荐具有较高的准确率和良好的置信度,提高了移动图书馆资源推荐的智能化水平和用户满意度。