Zhang Fan, Liu Yuling, Song Chao, Yang Chun, Hong Shaoyong
The Library of Guangzhou Huashang College, Guangzhou, China.
School of Data Science, Guangzhou Huashang College, Guangzhou, China.
PLoS One. 2024 Jan 26;19(1):e0297357. doi: 10.1371/journal.pone.0297357. eCollection 2024.
Library data contains many students' reading records that reflect their general knowledge acquisition. The purpose of this study is to deeply mine the library book-borrowing data, with concerns on different book catalogues and properties to predict the students' extracurricular interests. An intelligent computing framework is proposed by the fusion of a neural network architecture and a partial differential equations (PDE) function module. In model designs, the architecture is constructed as an adaptive learning backpropagation neural network (BPNN), with automatic tuning of its hyperparameters. The PDE module is embedded into the network structure to enhance the loss functions of each neural perceptron. For model evaluation, a novel comprehensive index is designed using the calculus of information entropy. Empirical experiments are conducted on a diverse and multimodal time-series dataset of library book borrowing records to demonstrate the effectiveness of the proposed methodology. Results validate that the proposed framework is capable of revealing the students' extracurricular reading interests by processing related book borrowing records, and expected to be applied to "big data" analysis for a wide range of various libraries.
图书馆数据包含许多学生的阅读记录，这些记录反映了他们的一般知识获取情况。本研究的目的是深入挖掘图书馆的图书借阅数据，关注不同的图书目录和属性，以预测学生的课外兴趣。通过融合神经网络架构和偏微分方程（PDE）函数模块，提出了一种智能计算框架。在模型设计中，该架构被构建为自适应学习反向传播神经网络（BPNN），并对其超参数进行自动调整。PDE模块被嵌入到网络结构中，以增强每个神经感知器的损失函数。在模型评估方面，利用信息熵演算设计了一种新颖的综合指标。对一个多样化的多模态图书馆图书借阅记录时间序列数据集进行了实证实验，以证明所提方法的有效性。结果验证了所提框架能够通过处理相关图书借阅记录揭示学生的课外阅读兴趣，并有望应用于广泛各类图书馆的“大数据”分析。
