Learning user profiles for personalized information dissemination
A. Tan, and C. Teo. Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, (May 1998)
DOI: 10.1109/ijcnn.1998.682259
Abstract
Personalized information systems represent the recent effort of
delivering information to users more effectively in the modern
electronic age. This paper illustrates how a supervised adaptive
resonance theory (ART) system, called fuzzy ARAM (adaptive resonance
associative map), can be used to learn user profiles for personalized
information dissemination. ARAM learning is online, fast, and
incremental. Acquisition of new knowledge does not require re-training
on previously learned cases. ARAM integrates both user-defined and
system-learned knowledge in a single framework. Therefore inconsistency
between the two knowledge sources will not arise. ARAM has been used to
develop a personalized news system (PIN). Preliminary experiments have
verified that PIN is able to provide personalized news by adapting to
user's interests in an online manner and generalizing them to new
information on-the-fly
%0 Journal Article
%1 citeulike:2537899
%A Tan, Ah-Hwee
%A Teo, C.
%B Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
%D 1998
%I IEEE
%J Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
%K neural-network personalization user-profile
%P 183--188 vol.1
%R 10.1109/ijcnn.1998.682259
%T Learning user profiles for personalized information dissemination
%U http://dx.doi.org/10.1109/ijcnn.1998.682259
%V 1
%X Personalized information systems represent the recent effort of
delivering information to users more effectively in the modern
electronic age. This paper illustrates how a supervised adaptive
resonance theory (ART) system, called fuzzy ARAM (adaptive resonance
associative map), can be used to learn user profiles for personalized
information dissemination. ARAM learning is online, fast, and
incremental. Acquisition of new knowledge does not require re-training
on previously learned cases. ARAM integrates both user-defined and
system-learned knowledge in a single framework. Therefore inconsistency
between the two knowledge sources will not arise. ARAM has been used to
develop a personalized news system (PIN). Preliminary experiments have
verified that PIN is able to provide personalized news by adapting to
user's interests in an online manner and generalizing them to new
information on-the-fly
%@ 0-7803-4859-1
@article{citeulike:2537899,
abstract = {{Personalized information systems represent the recent effort of
delivering information to users more effectively in the modern
electronic age. This paper illustrates how a supervised adaptive
resonance theory (ART) system, called fuzzy ARAM (adaptive resonance
associative map), can be used to learn user profiles for personalized
information dissemination. ARAM learning is online, fast, and
incremental. Acquisition of new knowledge does not require re-training
on previously learned cases. ARAM integrates both user-defined and
system-learned knowledge in a single framework. Therefore inconsistency
between the two knowledge sources will not arise. ARAM has been used to
develop a personalized news system (PIN). Preliminary experiments have
verified that PIN is able to provide personalized news by adapting to
user's interests in an online manner and generalizing them to new
information on-the-fly}},
added-at = {2017-11-15T17:02:25.000+0100},
author = {Tan, Ah-Hwee and Teo, C.},
biburl = {https://www.bibsonomy.org/bibtex/20747878531140c3adec9f2813f00bf57/brusilovsky},
booktitle = {Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on},
citeulike-article-id = {2537899},
citeulike-linkout-0 = {http://dx.doi.org/10.1109/ijcnn.1998.682259},
citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=682259},
doi = {10.1109/ijcnn.1998.682259},
interhash = {4a8b30ae9dd01c41464bdabb81ac55ae},
intrahash = {0747878531140c3adec9f2813f00bf57},
isbn = {0-7803-4859-1},
issn = {1098-7576},
journal = {Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on},
keywords = {neural-network personalization user-profile},
month = may,
pages = {183--188 vol.1},
posted-at = {2008-03-15 21:54:52},
priority = {2},
publisher = {IEEE},
timestamp = {2019-12-05T21:19:06.000+0100},
title = {{Learning user profiles for personalized information dissemination}},
url = {http://dx.doi.org/10.1109/ijcnn.1998.682259},
volume = 1,
year = 1998
}