Abstract
In Adaptive Information Filtering, the user profile has to be able to define and maintain an accurate representation of the user’s interests over time. According to Autopoietic Theory, the immune system faces a similar continuous learning problem. It is an organisationally closed network that reacts autonomously to define and preserve the organism’s identity. Nootropia is a user profiling model, which has been inspired by this view of the immune system. In this paper, we introduce new improvements to the model and propose a methodology for testing the ability of a user profile to continuously learn a user’s changing interests in a dynamic information environment. Comparative experiments show that Nootropia outperforms a popular learning algorithm, especially when more than one topic of interest has to be represented.
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Nanas, N., Vavalis, M., Kellis, L. (2009). Immune Learning in a Dynamic Information Environment. In: Andrews, P.S., et al. Artificial Immune Systems. ICARIS 2009. Lecture Notes in Computer Science, vol 5666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03246-2_20
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DOI: https://doi.org/10.1007/978-3-642-03246-2_20
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