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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 63-71    DOI: 10.11925/infotech.2096-3467.2018.1366
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Designing Framework for Precise Service of Scholarly Big Data
Jing Xie1,2(),Li Qian1,2,Hongbo Shi1,Beibei Kong1,Jiying Hu1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China
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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 wordsPrecise Service      Scholarly Big Data      Architecture Design      User Profile     
Received: 04 December 2018      Published: 04 March 2019

Cite this article:

Jing Xie,Li Qian,Hongbo Shi,Beibei Kong,Jiying Hu. Designing Framework for Precise Service of Scholarly Big Data. Data Analysis and Knowledge Discovery, 2019, 3(1): 63-71.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1366     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I1/63

[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.
[1] Guangshang Gao. A Survey of User Profiles Methods[J]. 数据分析与知识发现, 2019, 3(3): 25-35.
[2] Zhang Yunzhong, Yang Meng, Xu Baoxiang. Research on FCA-based User Profile Mining for Folksonomy[J]. 现代图书情报技术, 2011, 27(6): 72-78.
[3] Ku Liping. Model of Non-user-A Methodology for Information System Performance[J]. 现代图书情报技术, 2011, 27(1): 46-51.
[4] Ku Liping. Review of Personalized Interaction Design[J]. 现代图书情报技术, 2010, 26(11): 10-16.
[5] Wang Cuiying. Study on Folksonomies-based User Profiles Mining[J]. 现代图书情报技术, 2009, 25(6): 37-43.
[6] Li Shuqing. The Personalized Product Recommendation Method Based on Weighted XML Model[J]. 现代图书情报技术, 2009, 25(4): 64-69.
[7] Liu Rongfa. FC-SAN Storage System Construction and Performance Optimization for Digital Library[J]. 现代图书情报技术, 2008, 24(7): 70-74.
[8] Zhang Yulian,Wang Quan. User Profile Mining of Combining Web Behavior and Content Analysis[J]. 现代图书情报技术, 2007, 2(6): 52-55.
[9] Cui Jianhai,Cheng Ni,Wang Jun. Overview of Technologies of Personalized Web Information Retrieval[J]. 现代图书情报技术, 2005, 21(9): 45-49.
[10] Huang Xiaobin,Xia Mingchun,Ye Chuxuan. A Study on Filtering System Based on Digital Library[J]. 现代图书情报技术, 2004, 20(6): 6-10.
[11] Mei Haiyan. Research on Information Filtering[J]. 现代图书情报技术, 2002, 18(2): 44-47.
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