摘要
为提高网站知识服务质量,通过文献搜集及问卷调查确立网站知识服务质量评价标准,给出基于偏好分散算法的网站知识服务质量(QS)满意度数据分析,应用偏好分散算法(MUSA)对网站知识服务质量开展满意度实证研究。研究结果表明:基于MUSA来选择合适评估指标,减轻了使用者心理和认知过程的负担,具有较强实用性,使用者对所抽取的评估指标有较高的认同度。通过对3个网站的知识和业务指标满意程度开展分析。各网站均应该明确其竞争优势、劣势和需要改善的方向。该研究有助提高通信数据的用户体验度。
In order to improve and optimize the website knowledge service quality,the evaluation criteria of website knowledge service quality were established through literature collection and questionnaire survey,the website knowledge service quality(QS)satisfaction data analysis based on the preference decentralization algorithm was given,and the empirical research on the satisfaction of website knowledge service quality was conducted by applying the preference dispersion algorithm(MUSA).The research results show that the selection of appropriate evaluation indicators based on MUSA algorithm can reduce the burden of users′psychological and cognitive processes,and boasts strong practicability.Users also have a high degree for recognition for the extracted evaluation indicators.The satisfaction degree of knowledge and business indicators of three websites was analyzed.Each website should identify its competitive strengths,weaknesses and areas requiring improvement.This research can help improve the user experience level of communication-related data.
作者
秦明文
张立
李宏秋
QIN Mingwen;ZHANG Li;LI Hongqiu(Zhengzhou Campus,Army Artillery and Air Defense College,Zhengzhou 450064,China;Information Engineering University,Zhengzhou 450064,China)
出处
《技术与市场》
2026年第2期49-52,共4页
Technology and Market
关键词
网站知识服务
服务质量
偏好分散算法
满意度评价
website knowledge service
service quality
preference dispersion algorithm
satisfaction evaluation
