Please wait a minute...
New Technology of Library and Information Service  2002, Vol. 18 Issue (6): 24-27    DOI: 10.11925/infotech.1003-3513.2002.06.09
Current Issue | Archive | Adv Search |
Research on the Users' Model and Study Methods
Li Guangjian   Huang Kun
(School of Management, Beijing Normal University, Beijing 100875,China)
Download: PDF (0 KB)  
Export: BibTeX | EndNote (RIS)      
Abstract  

This article analyzes the users relevance and its influence on the users' satisfaction. It points out the functions of users modeling which can be used to record the personalized information and do modeling based learning and reasoning to find out the preference of users,and places an emphasis on users modeling during the process of information retrieval. It discusses the characteristic of users information requirements and addresses how to build the users modeling and realize the users' demands learning. Finally it gives a brief appraisal on the application of users modeling in the field of personalized information retrieval, also includes the difficulties.
Key words： Information retrieval      Users modeling      Users learning      Machine learning      Personalized information       service      Personalized information retrieval     
Received: 04 June 2002      Published: 25 December 2002
ZTFLH: 

G354

 		 
Corresponding Authors: Li Guangjian,Huang Kun   
About author:: Li Guangjian,Huang Kun
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors

Cite this article:

Li Guangjian,Huang Kun. Research on the Users' Model and Study Methods. New Technology of Library and Information Service, 2002, 18(6): 24-27.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2002.06.09     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2002/V18/I6/24

［１］　S. Mizzaro.“Relevance:The Whole History”.The Journal of the American Society for Information Science, 1997，48(9) P810-832
［２］　Langley, P. Machine learning for adaptive user interfaces. Proceedings of the 21st German Annual Conference on Artificial Intelligence (1997)，P53-62 Freiburg, Germany: Springer.
［３］　Lieberman, Henry. Integrating User Interface Agents With Convention Applications. Proceedings of the 1998 International Conference on Intelligent Interfaces. P39-46.
［４］　U. Shardanand and P.Maes.Social Information Filtering: Algorithms for Automating“Word of Mouth”. Proceedings of the Computer-Human Interaction Conference (CHI'95), Denver, Colorado, May 1995
［５］　马献明，严小卫，陈宏朝. 个性化网上信息代理技术的研究概述 . 广西师范大学学报(自然科学版)，2002，18(3)：40-44
［６］　汪晓岩，胡庆生，李斌，庄镇泉. 面向Internet的个性化智能信息检索 . 计算机研究与发展，1999；36(9)：1039-1046
［７］　李业丽，林鸿飞，姚天顺. 基于事例的用户信息需求模型. 用户计算机工程与应用 2000，(9)：11－13
［８］　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
[1] Chen Dong,Wang Jiandong,Li Huiying,Cai Sihang,Huang Qianqian,Yi Chengqi,Cao Pan. Forecasting Poultry Turnovers with Machine Learning and Multiple Factors[J]. 数据分析与知识发现, 2020, 4(7): 18-27.
[2] Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran. CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[3] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[4] Mingxuan Huang,Shoudong Lu,Hui Xu. Cross-Language Information Retrieval Based on Weighted Association Patterns and Rule Consequent Expansion[J]. 数据分析与知识发现, 2019, 3(9): 77-87.
[5] Ruojia Wang,Lu Zhang,Jimin Wang. Automatic Triage of Online Doctor Services Based on Machine Learning[J]. 数据分析与知识发现, 2019, 3(9): 88-97.
[6] Gang Li,Huayang Zhou,Jin Mao,Sijing Chen. Classifying Social Media Users with Machine Learning[J]. 数据分析与知识发现, 2019, 3(8): 1-9.
[7] Chuang Hong,He Li,Lihui Peng,Yiming Xu. Evaluating Information Services of Online Health Q&A Platform[J]. 数据分析与知识发现, 2019, 3(8): 41-52.
[8] Jiahui Hu,An Fang,Wanqing Zhao,Chenliu Yang,Huiling Ren. Annotating Chinese E-Medical Record for Knowledge Discovery[J]. 数据分析与知识发现, 2019, 3(7): 123-132.
[9] Jinzhu Zhang,Yiming Hu. Extracting Titles from Scientific References in Patents with Fusion of Representation Learning and Machine Learning[J]. 数据分析与知识发现, 2019, 3(5): 68-76.
[10] Zhiqiang Liu,Yuncheng Du,Shuicai Shi. Extraction of Key Information in Web News Based on Improved Hidden Markov Model[J]. 数据分析与知识发现, 2019, 3(3): 120-128.
[11] Hongxia Xu,Chunwang Li. Review of Knowledge Extraction of Scientific Literature[J]. 数据分析与知识发现, 2019, 3(3): 14-24.
[12] Jian Li,Mingyue Wang,Luming Xu,Yingchun Tian. The Construction of Digital Medical Information Service Evaluation System Based on User Perceived Value[J]. 数据分析与知识发现, 2019, 3(2): 118-126.
[13] Jing Li,Shuxiao Pan,Xueyan Li,Lijing Jia,Yuzhuo Zhao. Screening Critical Patients with Optimized Classifier Based on Multi Objective Quantum[J]. 数据分析与知识发现, 2019, 3(12): 101-112.
[14] Li Qian,Jing Xie,Zhijun Chang,Zhenxin Wu,Dongrong Zhang. Designing Smart Knowledge Services with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 4-14.
[15] Jiying Hu,Jing Xie,Li Qian,Changlei Fu. Constructing Big Data Platform for Sci-Tech Knowledge Discovery with Knowledge Graph[J]. 数据分析与知识发现, 2019, 3(1): 55-62.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn