基于广义加性模型的图书馆借阅预测研究

Analysis on the Application of Generalized Additive Model Based on Nesterov Acceleration in Library Lending Prediction

本文意图通过建立读者特征、不同类别图书流通量、读者借阅时间3者的关系模型,探索读者特征与借阅趋势之间的隐含规律,为图书馆的智慧管理提供可靠且快速的预测与分析。本文创新性地提出了基于广义加性模型(GAM)的3阶段快速拟合模型,采用Onehot编码、线性和非线性3种函数进行数据拟合,建立读者特征与图书流通的回归模型。考虑到图书馆数据的庞大性,本文利用Nesterov方法和Power Iteration方法对回归模型进行加速,在保证回归准确率的前提下,大幅度提高了算法速度。在真实图书馆数据上的实验表明,本文方法相较于纯线性模型准确性可以提高约70%,速度仅下降约30%;相较于纯非线性模型速度可以提高约6倍,而准确率仅下降约15%,较好地满足图书馆大规模数据的分析。

This paper intends to explore the implicit law between reader characteristics and borrowing trends by establishing a relationship model among reader characteristics,different types of book circulation,and reader borrowing time,so as to provide reliable and rapid prediction and analysis for the intelligent management of libraries.This paper innovatively proposes a three-stage fast fitting model based on generalized additive model(GAM),and uses one-hot coding,linear and nonlinear functions to fit data,and establishes a regression model between reader characteristics and book circulation.Considering the hugeness of library data,this paper uses Nesterov method and power iteration method to accelerate the regression model,which greatly improves the speed of the algorithm on the premise of ensuring the accuracy of the regression.Experiments on real library data show that the accuracy of the method in this paper can be improved by about 70%and the speed is only reduced by 30%compared with the pure linear model.Compared with the pure nonlinear model,the speed can be increased by about 6 times,and the accuracy rate is only reduced by about 15%,which is better for the analysis of large-scale data in libraries.