大数据环境下典型文献资源发现系统评测与建议  

Evaluation and Suggestion on Typical Literature Resource Discovery Systems in Big Data Environment

作　　者：李雪原[1] 张洁[2] LI XueYuan;ZHANG Jie(China Agricultural University Library,Beijing 100193,China;Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,China)

机构地区：[1]中国农业大学图书馆,北京100193 [2]中国农业科学院农业信息研究所,北京100081

出　　处：《农业大数据学报》2023年第3期83-92,共10页Journal of Agricultural Big Data

基　　金：国家新闻出版署农业融合出版知识挖掘与知识服务重点实验室开放基金项目“资源发现系统比较分析与评价”(项目编号:2021KMKS04)。

摘　　要：以Primo、Summon、EDS、百度学术、超星发现5种较为常用的发现系统为样本,梳理归纳发现系统的功能特点,研发、测试文献资源发现系统的评测指标体系并进行具体评测,调研分析用户在使用发现系统中获取信息的影响因素、系统易用性的影响因素、以及用户忠诚度的影响因素,同时结合大数据的发展与影响,提出6点发展建议:加强元数据的规范建设、提升文献信息的揭示度与获取途径、优化相关性排序、提升个性化服务、利用衍生数据推动精准服务、加强顶层规划,建立协同合作机制。Five commonly used discovery systems,Primo,Summon,EDS,Baidu Scholar and Chaoxing Discovery,are taken as samples to sort out and summarize the functional characteristics of the discovery system,develop and test the evaluation index system of the literature resource discovery system and conduct specific evaluation.The study investigates and analyzes the influencing factors of users'access to information in the use of the discovery system,the system usability,and user loyalty.Moreover,combined with the development and influence of big data,the study proposed six development suggestions:strengthen the normative construction of metadata,improve the disclosure and access of literature information,optimize the relevance ranking,improve personalized services,promote accurate services using derivative data,strengthen top-level planning and establish a mechanism for coordination and cooperation.

关 键 词：资源发现系统 知识发现系统 PRIMO SUMMON EDS 百度学术 超星发现系统 

分 类 号：G253[文化科学—图书馆学] TP311.13[自动化与计算机技术—计算机软件与理论]