Accuracy- and consistency-aware recommendation of configurations.

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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 publicationProceedings of the 26th ACM International Systems and Software Product Line Conference
PublisherAssociation of Computing Machinery
Number of pages6
ISBN (Electronic)9781450394437
Publication statusPublished - 12 Sep 2022
Event26th ACM International Systems and Software Product Line Conference: SPLC'22 - Graz, Austria
Duration: 12 Sep 202216 Sep 2022


Conference26th ACM International Systems and Software Product Line Conference
Abbreviated titleSPLC 2022
Internet address


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