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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1007/s11721-010-0044-6|
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Jerne’s idiotypic network theory stresses the importance of antibody-to-antibody interactions and provides possible explanations for self-tolerance and increased diversity in the immune repertoire. In this paper, we use an immune network model to build a user profile for adaptive information filtering. Antibody-to-antibody interactions in the profile’s network model correlations between words in text. The user profile has to be able to represent a user’s multiple interests and adapt to changes in them over time. This is a complex and dynamic engineering problem with clear analogies to the immune process of self-assertion. We present a series of experiments investigating the effect of term correlations on the user’s profile performance. The results show that term correlations can encode additional information, which has a positive effect on the profile’s ability to assess the relevance of documents to the user’s interests and to adapt to changes in them.
|Item Type:||Journal Article|
|Copyright Holders:||2010 Springer Science and Business Media LLC|
|Extra Information:||Special Issue: Artificial Immune Systems. Guest Editors: Jon Timmis, Paul S. Andrews and Emma Hart
|Keywords:||information filtering; adaptive information filtering; term networks; swarm; immune network; autopoiesis|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Anne De Roeck|
|Date Deposited:||01 Mar 2012 16:42|
|Last Modified:||05 Oct 2016 04:16|
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