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Learning condition-action rules for personalised journey recommendations

Karlsen, Matthew R. and Moschoyiannis, Sotiris (2018) Learning condition-action rules for personalised journey recommendations In: RuleML + RR 2018 International Conference, 18-21 Sep 2018, Luxemburg.

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Abstract

We apply a learning classifier system, XCSI, to the task of providing personalised suggestions for passenger onward journeys. Learn- ing 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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Karlsen, Matthew R.
Moschoyiannis, SotirisS.Moschoyiannis@surrey.ac.uk
Date : 24 August 2018
DOI : 10.1007/978-3-319-99906-7_21
Copyright Disclaimer : © Springer International Publishing AG 2018
Uncontrolled Keywords : Rule-based machine learning; XCSI; Passenger preferences
Related URLs :
Depositing User : Clive Harris
Date Deposited : 06 Jul 2018 13:10
Last Modified : 22 Sep 2018 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/848668

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