The semantic network approach used seems more powerful than the weighted keyword vector one. However it only addresses topic relevance and not context, which is my goal. The comparison between semantic structures and the spreading activation function can proove very usefull techniques. The feedback function used is basic and does not support the elimination of edges that are no longer express the user's interests.
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%0 Journal Article
%1 IA001
%A Sorensen, H.
%A Riordan, A. O'
%A Riordan, C. O'
%D 1997
%J Journal of Universal Computer Science
%K Text activation, feedback. filtering, network, relevance semantic spreading
%N 8
%P 988-1006
%T Profiling with the INFOrmer Text Filtering Agent
%V 3
@article{IA001,
added-at = {2009-06-22T10:05:09.000+0200},
author = {Sorensen, H. and Riordan, A. O' and Riordan, C. O'},
biburl = {https://www.bibsonomy.org/bibtex/248eace1f90c63101246a0c62aab99ffa/n.nanas},
interhash = {6e3c6318254cc1fc7c3a898366c1bc72},
intrahash = {48eace1f90c63101246a0c62aab99ffa},
journal = {Journal of Universal Computer Science},
keywords = {Text activation, feedback. filtering, network, relevance semantic spreading},
number = 8,
pages = {988-1006},
qnote = {The semantic network approach used seems more powerful than the weighted keyword vector one. However it only addresses topic relevance and not context, which is my goal. The comparison between semantic structures and the spreading activation function can proove very usefull techniques. The feedback function used is basic and does not support the elimination of edges that are no longer express the user's interests.},
timestamp = {2009-06-23T10:19:15.000+0200},
title = {Profiling with the INFOrmer Text Filtering Agent},
volume = 3,
year = 1997
}
