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Learning Condition–Action Rules for Personalised Journey Recommendations

  • Matthew R. Karlsen
  • Sotiris Moschoyiannis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11092)

Abstract

We apply a learning classifier system, XCSI, to the task of providing personalised suggestions for passenger onward journeys. Learning classifier systems combine evolutionary computation with rule-based machine learning, altering a population of rules to achieve a goal through interaction with the environment. Here XCSI interacts with a simulated environment of passengers travelling around the London Underground network, subject to disruption. We show that XCSI successfully learns individual passenger preferences and can be used to suggest personalised adjustments to the onward journey in the event of disruption.

Keywords

Rule-based machine learning XCSI Passenger preferences 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SurreyGuildfordUK

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