科研学术大数据的精准服务架构设计<sup>*</sup>

doi:10.11925/infotech.2096-3467.2018.1366

全文: PDF(3131 KB) HTML ( 5 )
输出: BibTeX | EndNote (RIS)

摘要

【目的】研究面向科研学术大数据的精准服务架构, 解决科研学者获取精准知识服务的问题。【方法】通过分析互联网应用精准服务现状, 从数据组织、技术方法、应用场景三个方面, 归纳对比精准服务的方法, 面向科研学术生态链条设计精准服务体系。【结果】精准服务架构设计梳理关联了数据生产、技术研发、应用服务环节, 支撑了科研学术的精准搜索与推荐服务应用。【局限】该架构未与传统方法对比, 还需结合实际应用评测验证精准性提升效果。【结论】本文解决了精准服务技术方法的单一性问题, 为应用系统研发提供系统的架构支撑。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	谢靖
	钱力
	师洪波
	孔贝贝
	胡吉颖

关键词 ：精准服务, 学术大数据, 架构设计, 用户画像

Abstract：

[Objective] This paper proposes a framework for precise service of scholarly big data, aiming to improve knowledge acquisition of researchers. [Methods] First, we analyzed the status quo of online precision services. Then we summarized and compared the methods of precision services from the perspectives of data organization, technical methods and application scenarios. Finally, we designed the framework for academic eco-chain of scientific research. [Results] The framework connected data production, technology research and application development, which supported the precise search and recommendation of sci-tech data. [Limitations] More research is needed to evaluate the framework with real-world cases. [Conclusions] This proposed framework could help us build better academic precision search systems.

Key words： Precise Service Scholarly Big Data Architecture Design User Profile

收稿日期: 2018-12-04

基金资助:*本文系中国科学院文献情报能力建设专项项目“基于大数据计算的知识发现服务平台建设”(项目编号: 院1853)的研究成果之一

引用本文:

谢靖,钱力,师洪波,孔贝贝,胡吉颖. 科研学术大数据的精准服务架构设计^*[J]. 数据分析与知识发现, 2019, 3(1): 63-71.
Jing Xie,Li Qian,Hongbo Shi,Beibei Kong,Jiying Hu. Designing Framework for Precise Service of Scholarly Big Data. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2018.1366.

链接本文:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1366

[1]	Gomez-Uribe C A, Hunt N. The Netflix Recommender System: Algorithms, Business Value and Innovation[J]. ACM Transactions on Management Information Systems, 2016, 6(4): 1-19.
[2]	曹欢欢. 今日头条算法原理[EB/OL]. [2018-01-16]..
[2]	(Cao Huanhuan.Algorithm Principle of Today’s Headline[EB/OL]. [2018-01-16]..)
[3]	洪亮, 任秋圜, 梁树贤. 国内电子商务网站推荐系统信息服务质量比较研究——以淘宝、京东、亚马逊为例[J]. 图书情报工作, 2016, 60(23): 97-110.
[3]	(Hong Liang, Ren Qiuyuan, Liang Shuxian.A Comparative Study of Information Service Quality of E-commerce Sites’ Recommender Systems —— Take Taobao, Jingdong and Amazon as Examples[J]. Library and Information Service, 2016, 60(23): 97-110.)
[4]	王贤慧, 袁军鹏. 一种面向社会关系的同行评议方法[J]. 科技管理研究, 2017(23): 228-232.
[4]	(Wang Xianhui, Yuan Junpeng.A Study on Peer Review by Experts Based on Social Relationship[J]. Science and Technology Management Research, 2017(23): 228-232.)
[5]	York A.Next-generation Bacterial Taxonomy[J]. Nature Reviews Microbiology, 2018, 16(10): 583.
[6]	Tudhope D, Nielsen M L.Introduction to Knowledge Organization Systems and Services[J]. New Review of Hypermedia and Multimedia, 2006, 12(1): 3-9.
[7]	陈慧香, 邵波. 国外图书馆领域用户画像的研究现状及启示[J]. 图书馆学研究, 2017(20): 16-20.
[7]	(Chen Huixiang, Shao Bo.The Research Status and Enlightenment of the User Profile in the Library Field at Abroad[J]. Researches in Library Science, 2017(20): 16-20.)
[8]	Dumais S T.Latent Semantic Analysis[J]. Information Science and Technology, 2004, 38(1): 188-230.
[9]	Thomo A. Lantent Semantic Analysis (Tutorial) [EB/OL]. [2013-10-01]. .
[10]	Francesco R, Lior R, Bracha S, et al.Recommender Systems Handbook[M]. Springer US, 2015: 1-34.