Prediction of Book Borrowing Topic Heat Based on Latent Dirichlet Allocation and Bidirectional Gate Recurrent Unit
The analysis of book borrowing theme can mine read borrowing preferences and reading rules of readers.By using the prediction model of borrowing theme heat,it can predict the change trend of borrowing theme heat,which is of great significance for libraries to carry out reading promotion activities.In order to solve the problem of book borrowing topic extraction and topic heat prediction,this paper proposes a borrowing topic heat prediction model based on LDA and bidirectional GRU neural network.The algorithm extracts the borrowing book features and borrowing topics of readers in different time periods through LDA algorithm.On the basis of calculating the heat of borrowing topics in dif-ferent time periods and constructing the data set of borrowing topic heat sequence,a topic heat prediction model based on bidirectional GRU neural network is constructed to predict the change trend of future topic heat,and the experimental evaluation is carried out on the paper litera-ture borrowing record data set of Xiamen University Library.The simulation results show that the model can accurately obtain the relationship between borrowing topics and keywords,and compared with algorithms such as machine learning,the model can effectively reduce the predic-tion error of borrowing topics.
国家科技期刊平台
NSTL
万方数据
