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Title:
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USER PROFILING FROM CITIZEN WEB PORTAL ACCESSES USING THE ADAPTIVE RESONANCE THEORY NEURAL NETWORK |
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Author(s):
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José D. Martín-guerrero , Emilio Soria-olivas , Paulo J.g. Lisboa , Alberto Palomares , Emili Balaguer-ballester |
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ISBN:
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972-8924-16-X |
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Editors:
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Pedro Isaías, Maggie McPherson and Frank Bannister |
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Year:
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2006 |
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Edition:
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2 |
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Keywords:
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Adaptive Resonance Theory, Citizen Web Portal, User Profiling, Clustering. |
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Type:
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Short Paper |
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First Page:
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334 |
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Last Page:
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337 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In this paper, we propose the use of the Adaptive Resonance Theory, and more specifically the ART2 neural network for
carrying out a clustering of web users; this is because of its capabilities to find clusters regardless of whether the clusters
present a certain size or shape. Moreover, this algorithm does not need to know the number of clusters in advance. In
order to evaluate the goodness of the modelling, six artificial data sets covering usual situations that one can find in real
sites are created; clustering achieved by ART2 are benchmarked with the classical K-means algorithm. Afterwards, we
apply the clustering algorithms to a real data set which records accesses of users to the web citizen portal Infoville XXI.
The clusters found by the ART2 with the real data set are very straightforward and easy to interpret, providing a useful
basis for the design of a personalized recommender system. |
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