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Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems

  • Marcel HildebrandtEmail author
  • Swathi Shyam Sunder
  • Serghei Mogoreanu
  • Ingo Thon
  • Volker Tresp
  • Thomas Runkler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

Building complex automation solutions, common to process industries and building automation, requires the selection of components early on in the engineering process. Typically, recommender systems guide the user in the selection of appropriate components and, in doing so, take into account various levels of context information. Many popular shopping basket recommender systems are based on collaborative filtering. While generating personalized recommendations, these methods rely solely on observed user behavior and are usually context free. Moreover, their limited expressiveness makes them less valuable when used for setting up complex engineering solutions. Product configurators based on deterministic, handcrafted rules may better tackle these use cases. However, besides being rather static and inflexible, such systems are laborious to develop and require domain expertise. In this work, we study various approaches to generate recommendations when building complex engineering solutions. Our aim is to exploit statistical patterns in the data that contain a lot of predictive power and are considerably more flexible than strict, deterministic rules. To achieve this, we propose a generic recommendation method for complex, industrial solutions that incorporates both past user behavior and semantic information in a joint knowledge base. This results in a graph-structured, multi-relational data description – commonly referred to as a knowledge graph. In this setting, predicting user preference towards an item corresponds to predicting an edge in this graph. Despite its simplicity concerning data preparation and maintenance, our recommender system proves to be powerful, as shown in extensive experiments with real-world data where our model outperforms several state-of-the-art methods. Furthermore, once our model is trained, recommending new items can be performed efficiently. This ensures that our method can operate in real time when assisting users in configuring new solutions.

Keywords

Recommender system Cold start Knowledge graph Link prediction Tensor factorization 

Notes

Acknowledgements

We would like to thank Siemens Digital Factory Division for providing the data and helping us to get a better understanding of the application area, our colleague Martin Ringsquandl for the insightful discussions and helpful remarks on early drafts, as well as the head of our research group, Steffen Lamparter, for providing all the necessary resources.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marcel Hildebrandt
    • 1
    • 2
    Email author
  • Swathi Shyam Sunder
    • 1
    • 3
  • Serghei Mogoreanu
    • 1
  • Ingo Thon
    • 1
  • Volker Tresp
    • 1
    • 2
  • Thomas Runkler
    • 1
    • 3
  1. 1.Siemens AG, Corporate TechnologyMunichGermany
  2. 2.Ludwig Maximilian UniversityMunichGermany
  3. 3.Technical University of MunichMunichGermany

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