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Towards Similarity-Aware Constraint-Based Recommendation

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11606)

Abstract

Constraint-based recommender systems help users to identify useful objects and services based on a given set of constraints. These decision support systems are often applied in complex domains where millions of possible recommendations exist. One major challenge of constraint-based recommenders is the identification of recommendations which are similar to the user’s requirements. Especially, in cases where the user requirements are inconsistent with the underlying constraint set, constraint-based recommender systems have to identify and apply the most suitable diagnosis in order to identify a recommendation and to increase the user’s satisfaction with the recommendation. Given this motivation, we developed two different approaches which provide similar recommendations to users based on their requirements even when the user’s preferences are inconsistent with the underlying constraint set. We tested our approaches with two real-world datasets and evaluated them with respect to the runtime performance and the degree of similarity between the original requirements and the identified recommendation. The results of our evaluation show that both approaches are able to identify recommendations of similar solutions in a highly efficient manner.

Keywords

  • Decision support systems
  • Constraint-based recommender systems
  • Similarity measures
  • Recommendation similarity

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Fig. 1.

Notes

  1. 1.

    The work presented in this paper has been partially conducted within the scope of the research projects WeWant (basic research project funded by the Austrian Research Promotion Agency - 850702) and OpenReq (Horizon 2020 project funded by the European Union - 732463).

  2. 2.

    If this information is not provided, equal importance of all variables is assumed.

  3. 3.

    Choco [16] is a free open-source constraint solver library for the Java programming language. http://www.choco-solver.org/.

  4. 4.

    https://www.itu.dk/research/cla/externals/clib/, Maintained by CLA group. KB definition in CSP representation: https://github.com/CSPHeuristix/CDBC/.

  5. 5.

    All user requirements were inconsistent with the underlying KB.

  6. 6.

    Our approaches were implemented in programming language Java and were executed on a computer with following properties: Windows 10 Enterprise; 64-bit operating system; Intel(R) Core(TM) i5-5200 CPU @ 2,20 GHz processor; 8,00 GB RAM.

  7. 7.

    For training and testing our approaches, we automatically generated again 500 user requirements. All user requirements were inconsistent with the underlying KB.

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Acknowledgments

The work presented in this paper has been conducted within the scope of the research projects WeWant (basic research project funded by the Austrian Research Promotion Agency) and OpenReq (Horizon 2020 project funded by the European Union - 732463).

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Correspondence to Muesluem Atas .

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Atas, M., Tran, T.N.T., Felfernig, A., Erdeniz, S.P., Samer, R., Stettinger, M. (2019). Towards Similarity-Aware Constraint-Based Recommendation. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_26

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  • DOI: https://doi.org/10.1007/978-3-030-22999-3_26

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