Accuracy- and consistency-aware recommendation of configurations.

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Constraint-based configurators support users in deciding which components and features should be included in a configuration. Due to the increasing size and complexity of configurable products and services, recommender systems are used to personalize the interaction with configurators. Since basic recommendation approaches such as collaborative filtering do not take into account constraints between variable values, recommendations can induce inconsistencies between user requirements and the underlying configuration knowledge base. In this paper, we introduce a constraint-based configuration approach that integrates the results of model-based collaborative filtering (e.g., implemented as feed forward neural network) into constraint solving in such a way that the solver (configurator) is able to determine consistency-preserving and user-relevant configurations.
Original languageEnglish
Title of host publication26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings
EditorsAlexander Felfernig, Lidia Fuentes, Jane Cleland-Huang, Wesley K.G. Assuncao, Wesley K.G. Assuncao, Andreas Falkner, Maider Azanza, Miguel A. Rodriguez Luaces, Megha Bhushan, Laura Semini, Xavier Devroey, Claudia Maria Lima Werner, Christoph Seidl, Viet-Man Le, Jose Miguel Horcas
PublisherAssociation of Computing Machinery
Number of pages6
ISBN (Electronic)9781450394437
Publication statusPublished - 12 Sept 2022
Event26th ACM International Systems and Software Product Line Conference: ASPLC 2022 - Graz, Austria
Duration: 12 Sept 202216 Sept 2022

Publication series

Name26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings


Conference26th ACM International Systems and Software Product Line Conference
Abbreviated titleSPLC'22
Internet address


  • collaborative filtering
  • configuration
  • constraint solving
  • feature models
  • neural networks

ASJC Scopus subject areas

  • Software

Cite this