摘要
【目的/意义】为了改善传统评价的主观性和模糊性,更好地体现高校图书馆数字资源用户需求,在用户贡献指标的基础上构建服务质量评价体系,基于灰色关联分析构建GA-BPNN模型用于高校图书馆数字资源服务质量评价。【方法/过程】从用户需求的角度征询用户意见,提炼影响服务质量评价的关键要素,在实证分析的基础上构建了一个相对科学、合理的初步评价模型。然后采集样本,以灰色关联度表征服务质量评价结果。最后应用MATLAB进行仿真实验,对比分析GA-BPNN模型及标准BPNN模型的表现优劣。【结果/结论】GA优化后的BPNN的性能得到改善,预测值更接近真实值,容错性高,稳定性好,期望对高校及其他图书馆数字资源建设、服务质量评价及服务创新研究有参考作用。【创新/局限】提出一种基于用户需求的高校图书馆数字资源服务质量评价方法,主要局限是调查数据覆盖范围不够全面。
【Purpose/significance】 In order to improve the subjectivity and fuzziness of traditional evaluation and better reflect the demands of users,an evaluation system of digital resource service quality of academic libraries is constructed on the basis of the indexes that users contribute,and using grey correlation analysis.GA-BPNN model is proposed to evaluate the service quality of digital resources of academic libraries.【Method/process】The paper consults users’ opinions from the perspective of their demands,extracts the key elements that affect the evaluation of digital resource service quality in academic libraries,and builds a relatively scientific and reasonable hypothetical model by means of empirical analysis.Then,samples are collected and the results of service quality evaluation are represented by grey correlation degree.Finally,MATLAB R2019 b is used to conduct simulation experiments,the performance of GABPNN model and BPNN model are compared and analyzed.【Results/conclusions】The actual output of GA-BPNN is close to user’s subjective evaluation results,and the accuracy and stability of GA-BPNN are significantly better than the BPNN model,which reflects the feasibility and rationality of the evaluation model.It is expected to provide some references for the research on digital resources construction,service quality evaluation and service innovation of academic libraries and other libraries.【Innovation/limitation】The paper puts forward a method to evaluate academic libraries’ digital resource service quality based on user demand.The main limitation is that the coverage of survey data is not comprehensive enough.
作者
朱学芳
邢绍艳
ZHU Xue-fang;XING Shao-yan(School of Information Management,Nanjing University,Nanjing 210023,China)
出处
《情报科学》
CSSCI
北大核心
2022年第3期3-11,20,共10页
Information Science
关键词
用户需求
数字资源
服务质量评价
遗传算法
BP神经网络
user demand
digital resources
service quality evaluation
genetic algorithm
BP Neural Network