Please wait a minute...
New Technology of Library and Information Service  2002, Vol. 18 Issue (3): 48-50    DOI: 10.11925/infotech.1003-3513.2002.03.15
Current Issue | Archive | Adv Search |
Method and Relative Technologies on Network Information Filtering
Liu Weicheng   Jiao Yuying
(School of Information Management, Wuhan University, Wuhan 430072,China)
Download: PDF (0 KB)  
Export: BibTeX | EndNote (RIS)      
Abstract  

With the development of Internet the problem of information overloading appeared. In order to provide personalized and practical information to users, information filtering method is put forward at the historic moment. According to domestic and abroad achivement this article discusses network information filtering method and technology in four respects such as expressing user's information needs, text expressing method, information matching method and information feedback method, and existing problem is also proposed.

Key wordsNetwork      Information filtering      Filtering method     
Received: 08 October 2001      Published: 25 June 2002
ZTFLH: 

G354.2

 
Corresponding Authors: Liu Weicheng,Jiao Yuying   
About author:: Liu Weicheng,Jiao Yuying

Cite this article:

Liu Weicheng,Jiao Yuying. Method and Relative Technologies on Network Information Filtering. New Technology of Library and Information Service, 2002, 18(3): 48-50.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2002.03.15     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2002/V18/I3/48

[1] elkin N J, Croft W B.Information filtering and information retrieval: two sides of the same coin. Communication of ACM, 1992, 35(1):2 9-38
[2] Pazzani M, Billsus D.Learning and revising user profiles: The Identification of Interesting Web Sites. Machine Learning, 1997, 27:313-331
[3] Armstrong R, Freitag D, Joachims T, et al. Web watcher: A Learning apprentice for the world wide Web. Working Notes of the AAAI Spring Symposium Series on Information Gathering from Distributed, Heterogeneous Environments, 1995, 6-12
[4] Lieberman H.Letizia:An agent that assists Web browsing. Proceedings of the international Joint Conference on Artificial Interlligence, 1995. 924-929
[5] 田范江,李丛蓉,王鼎兴.进化式信息过滤方法研究.软件学报2000,11(3):328-333
[6] 田忠和,王明哲.基于特征的贝叶斯过滤网.华中理工大学学报,1999,27(1):17-19
[7] 傅忠谦,王新跃,周佩玲,彭虎,陶小丽.个性化网上信息过滤智能体的实现.计算机应用,2000,20(3):26-29
[8] Lashkari Y.The webhound personalized document filtering system. http://rg.media.mit.edu/projects/webhound/,1996
[9] 卢增祥,路海明,李衍达.网络信息过滤中的固定文章集表达方法.清华大学学报(自然科学版),1999,39(9):118-121
[10] 林鸿飞,姚天顺.基于示例的中文文本过滤模型.大连理工大学学报,2000,40(3):375-378
[11] 杨清,杨岳湘,瞿国平.智能移动式定题检索Agent的研究与设计.计算机应用与软件,2000,12:1-6
[12] 林鸿飞,战学刚,姚天顺.文本结构分析与基于示例的文本过滤.小型微型计算机系统,2000,21(4):422-425
[13] 卢增祥,路海明,李衍达.利用Bookmark服务进行网络信息过滤.软件学报2000,11(4):545-550
[14] Pazzani M, Billsus D.Learning and revising user prifles: the identification of interesting Web sites. Machine Learning, 1997, 27(3):313-331
[15] 焦李成、保铮.进化计算与遗传算法——计算智能的新方向.系统工程与电子技术,1995,17(6):20-32

[1] Xi Yunjiang, Du Diedie, Liao Xiao, Zhang Xuehong. Analyzing & Clustering Enterprise Microblog Users with Supernetwork[J]. 数据分析与知识发现, 2020, 4(8): 107-118.
[2] Sheng Jiaqi, Xu Xin. Expanding Scholar Labels with Research Similarity and Co-authorship Network[J]. 数据分析与知识发现, 2020, 4(8): 75-85.
[3] Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[4] Cai Yongming,Liu Lu,Wang Kewei. Identifying Key Users and Topics from Online Learning Community[J]. 数据分析与知识发现, 2020, 4(6): 69-79.
[5] Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[6] Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
[7] Li Wenzheng,Gu Yijun,Yan Hongli. Predicting Community Numbers with Network Bayesian Information Criterion[J]. 数据分析与知识发现, 2020, 4(4): 72-82.
[8] Yan Chun,Liu Lu. Classifying Non-life Insurance Customers Based on Improved SOM and RFM Models[J]. 数据分析与知识发现, 2020, 4(4): 83-90.
[9] Su Chuandong,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Mao Junyu,Zhu Jiaying,Pan Yuhao. Identifying Chinese / English Metaphors with Word Embedding and Recurrent Neural Network[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
[10] Liu Yuwen,Wang Kai. Finding Geographic Locations of Popular Online Topics[J]. 数据分析与知识发现, 2020, 4(2/3): 173-181.
[11] Xu Yuemei,Liu Yunwen,Cai Lianqiao. Predicitng Retweets of Government Microblogs with Deep-combined Features[J]. 数据分析与知识发现, 2020, 4(2/3): 18-28.
[12] Xu Jianmin,Zhang Liqing,Wang Miao. Tracking Static Topics with Bayesian Network[J]. 数据分析与知识发现, 2020, 4(2/3): 200-206.
[13] Xiang Fei,Xie Yaotan. Recognition Model of Patient Reviews Based on Mixed Sampling and Transfer Learning[J]. 数据分析与知识发现, 2020, 4(2/3): 39-47.
[14] Yu Chuanming,Zhong Yunci,Lin Aochen,An Lu. Author Name Disambiguation with Network Embedding[J]. 数据分析与知识发现, 2020, 4(2/3): 48-59.
[15] Ni Weijian,Guo Haoyu,Liu Tong,Zeng Qingtian. Online Product Recommendation Based on Multi-Head Self-Attention Neural Networks[J]. 数据分析与知识发现, 2020, 4(2/3): 68-77.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn