图书馆图书信息用户快速自动查阅仿真研究

发布时间：2018-06-27 06:37

本文选题：图书馆 + 图书信息　；参考：《计算机仿真》2017年09期

【摘要】：通过对图书馆图书信息查阅问题的研究,能够有效提高用户对图书信息的查阅效率。对图书信息的快速查阅,需要给出不同类型资源关联度度量阈值,构建新的图书信息查阅特征表达式,完成用户对图书信息的查阅。传统方法通过图书信息约束条件,组建匹配两步投影矩阵,但忽略了特征表达式的构建,导致查阅效率、精度较低。提出基于概率生成的图书馆图书信息快速自动查阅方法。融合于概率指派生成理论组建一个图书信息概率生成模型,将图书信息的未知类型定义为模型的潜在变量,计算出联合概率分布,获取各个类型资源特征分布的后验估计值,给出不同类型资源关联度度量阈值,构建匹配特征表达式,计算出相邻类型图书信息特征间的最短距离,完成用户对图书馆图书信息的快速查阅。实验结果表明,所提方法查阅精度高,可以为提升图书馆图书信息查阅速度奠定了结实的基础。
[Abstract]:Through the research of the library information consultation, it can effectively improve the efficiency of the users' reference to the book information. For the quick reference of book information, it is necessary to give the threshold value of different types of resource correlation degree, construct a new characteristic expression of book information retrieval, and complete the search of book information by users. The traditional method sets up the matching two-step projection matrix through the book information constraint condition, but neglects the construction of the characteristic expression, which leads to the search efficiency and the low precision. This paper presents a method of library information retrieval based on probability generation. Combining the theory of probability assignment generation, a probability generation model of book information is constructed. The unknown type of book information is defined as the potential variable of the model, the joint probability distribution is calculated, and the posteriori estimation value of the characteristic distribution of each type of resource is obtained. This paper gives the threshold value of the correlation degree of different types of resources, constructs the expression of matching feature, calculates the shortest distance between the information features of the adjacent types of books, and accomplishes the quick search of library information by users. The experimental results show that the proposed method has high accuracy and can lay a solid foundation for improving the speed of library information retrieval.
【作者单位】：河南中医药大学网络信息中心;
【基金】：河南省软科学研究项目(162400410097)
【分类号】：G252

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