Abstract
The immense amount of unstructured information available on the Web poses increasing difficulties to fulfill users’ needs. New tools are needed to automatically collect and filter information that meets users’ demands. This paper presents the architecture of a personal information agent that mines web sources and retrieves documents according to users’ interests. The agent operates in two modes: "generation of space of concepts" and "document filtering". A space of concepts for a domain is represented by a matrix of asymmetrical coefficients of similarity for each pair of relevant terms in the domain. This matrix is seen as a Hopfield neural network, used for document filtering, where terms represent neurons and the coefficients of similarity the weights of the links that connect the neurons. Experiments conducted to evaluate the approach show that it exhibits satisfactory effectiveness.
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© 2007 Springer-Verlag Berlin Heidelberg
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Marin, A., Adán-Coello, J.M., Rosa, J.L.G., Tobar, C.M., de Freitas, R.L. (2007). Personalized Web Page Filtering Using a Hopfield Neural Network. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_43
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DOI: https://doi.org/10.1007/978-3-540-74695-9_43
Publisher Name: Springer, Berlin, Heidelberg
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