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Providing Intelligent Assistance for Product Configuration in Manufacturing: A Learning-to-Rank Approach

  • Carsten Poggemeier
  • Matthias Hartung
  • Philipp Cimiano
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 15)

Abstract

Configuring complex products can be a challenge due to the huge number of configuration possibilities. In this paper, our goal is to foster the development of intelligent configuration assistants that can support customers in configuring complex products. We formalize the task as a machine learning problem and in particular as a learning-to-rank problem. Given pairwise preferences elicited from experts, we show that we can train a model using support vector machines that ranks possible products according to their relevance to a given set of requirements specified by a user.

Keywords

Product configuration Ranking Information retrieval 

Notes

Acknowledgment

The second and third author acknowledge funding from the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277), Bielefeld University.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Carsten Poggemeier
    • 1
  • Matthias Hartung
    • 2
  • Philipp Cimiano
    • 2
  1. 1.HARTING IT Services GmbH & Co. KGEspelkampGermany
  2. 2.CITECBielefeld UniversityBielefeldGermany

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