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

Content Provider	Springer Nature Link
Author	Bouchachia, Abdelhamid Lena, Arthur Vanaret, Charlie
Copyright Year	2013
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.
Starting Page	143
Ending Page	157
Page Count	15
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	5
Issue Number	3
e-ISSN	18686486
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-28
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Online optimization Incremental evolutionary algorithms User profile learning Information filtering Self-adaptation Self-evolving Complexity Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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