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MASACAD: A Multiagent-Based Approach to Information Customization
January/February 2006 (vol. 21 no. 1)
pp. 60-67
ASCII Text
x 
 
Mohamed Salah Hamdi, "MASACAD: A Multiagent-Based Approach to Information Customization," IEEE Intelligent Systems, vol. 21, no. 1, pp. 60-67, January/February, 2006.
 
 
BibTex
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@article{ 10.1109/MIS.2006.14,
author = {Mohamed Salah Hamdi},
title = {MASACAD: A Multiagent-Based Approach to Information Customization},
journal ={IEEE Intelligent Systems},
volume = {21},
number = {1},
issn = {1541-1672},
year = {2006},
pages = {60-67},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2006.14},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - MASACAD: A Multiagent-Based Approach to Information Customization
IS - 1
SN - 1541-1672
SP60
EP67
EPD - 60-67
A1 - Mohamed Salah Hamdi,
PY - 2006
KW - academic advising
KW - information customization
KW - multiagents
KW - neural networks
KW - Web mining
KW - expert systems
VL - 21
JA - IEEE Intelligent Systems
ER -
 
Mohamed Salah Hamdi, University of Qatar
The huge amount of content available on the World Wide Web raises important questions over its effective use. To cope with such environments, the promise of information customization systems is becoming highly attractive. MASACAD is a multiagent system that learns to advise students by mining the Web. It approaches information customization using a multiagent paradigm in combination with aspects from machine learning, user modeling, and Web mining.

This article is part of a special issue on AI, Agents, and the Web.

Index Terms:
academic advising, information customization, multiagents, neural networks, Web mining, expert systems
Citation:
Mohamed Salah Hamdi, "MASACAD: A Multiagent-Based Approach to Information Customization," IEEE Intelligent Systems, vol. 21, no. 1, pp. 60-67, Jan./Feb. 2006, doi:10.1109/MIS.2006.14
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