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Online and interactive self-adaptive learning of user profile using incremental evolutionary algorithms

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Abstract

In this contribution, we explore the application of evolutionary algorithms for information filtering. There are two crucial issues we consider in this study: (1) the generation of the user’s profile which is the central task of any information filtering or routing system; (2) self-adaptation and self-evolving of the user’s profile given the dynamic nature of information filtering. Basically the problem is to find the set of weighted terms that best describe the interests of the user. Thus, the problem of user profile generation can be perceived as an optimization problem. Moreover, because the user’s interests are obtained implicitly and continuously over time from the relevance feedback of the user, the optimization process must be incremental and interactive. To meet these requirements, an incremental evolutionary algorithm that updates the profile over time as new feedback becomes available is introduced. New genetic operators (crossover and mutation) fitting the application at hand are proposed. Moreover, methods for feature selection, incremental update of the profile and multi-profiling are devised. The experimental investigations show that the proposed approach including the individual methods for the different aspects is suitable and provides high performance rates on real-world data sets.

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Notes

  1. In the context of this study by ”information filtering” (IF) we refer to “text filtering” (TF) and we will use them interchangeably

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Authors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, Dorset, UK

    Abdelhamid Bouchachia

  2. University of Toulouse, Toulouse, France

    Arthur Lena & Charlie Vanaret

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  1. Abdelhamid Bouchachia
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  2. Arthur Lena
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  3. Charlie Vanaret
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Correspondence to Abdelhamid Bouchachia.

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Bouchachia, A., Lena, A. & Vanaret, C. Online and interactive self-adaptive learning of user profile using incremental evolutionary algorithms. Evolving Systems 5, 143–157 (2014). https://doi.org/10.1007/s12530-013-9096-3

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