Skip to main content

Towards Similarity-Aware Constraint-Based Recommendation

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


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-22999-3_26
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-22999-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Fig. 1.


  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.

  4. 4., Maintained by CLA group. KB definition in CSP representation:

  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.


  1. Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inf. Syst. 32(2000), 175–185 (2000)

    Google Scholar 

  2. Dabrowski, M., Acton, T.: Beyond similarity-based recommenders: preference relaxation and product awareness. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 296–307. Springer, Heidelberg (2011).

    CrossRef  Google Scholar 

  3. de Kleer, J., Mackworth, A.K., Reiter, R.: Readings in model-based diagnosis. In: Characterizing Diagnoses and Systems, pp. 54–65. Morgan Kaufmann Publishers Inc., San Francisco (1992)

    Google Scholar 

  4. Eiter, T., Erdem, E., Erdoğan, H., Fink, M.: Finding similar or diverse solutions in answer set programming. In: Hill, P.M., Warren, D.S. (eds.) ICLP 2009. LNCS, vol. 5649, pp. 342–356. Springer, Heidelberg (2009).

    CrossRef  Google Scholar 

  5. Felfernig, A., Atas, M., Tran, T.N.T., Stettinger, M., Erdeniz, S.P., Leitner, G.: An analysis of group recommendation heuristics for high- and low-involvement items. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017. LNCS (LNAI), vol. 10350, pp. 335–344. Springer, Cham (2017).

    CrossRef  Google Scholar 

  6. Felfernig, A., Schubert, M., Reiterer, S.: Personalized diagnosis for over-constrained problems. In: Proceedings of the Twenty-Third International Joint Conference on AI, IJCAI 2013, pp. 1990–1996. AAAI Press (2013)

    Google Scholar 

  7. Gasparic, M., Janes, A.: What recommendation systems for software engineering recommend. J. Syst. Softw. 113(C), 101–113 (2016)

    CrossRef  Google Scholar 

  8. Hebrard, E., Hnich, B., O’Sullivan, B., Walsh, T.: Finding diverse and similar solutions in constraint programming. In: Proceedings of the 20th National Conference on Artificial Intelligence, AAAI 2005, vol. 1, pp. 372–377. AAAI Press (2005)

    Google Scholar 

  9. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction, 1st edn. Cambridge University Press, New York (2010)

    CrossRef  Google Scholar 

  10. Junker, U.: Quickxplain: preferred explanations and relaxations for over-constrained problems. In: Proceedings of the 19th National Conference on Artifical Intelligence, AAAI 2004, pp. 167–172. AAAI Press (2004)

    Google Scholar 

  11. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)

    CrossRef  Google Scholar 

  12. McSherry, D.: Similarity and compromise. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 291–305. Springer, Heidelberg (2003).

    CrossRef  MATH  Google Scholar 

  13. McSherry, D.: Maximally successful relaxations of unsuccessful queries. In: 15th Conference on AI and Cognitive Science, pp. 127–136. AAAI Press (2004)

    Google Scholar 

  14. Paraschakis, D.: Recommender systems from an industrial and ethical perspective. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 463–466. ACM, New York (2016)

    Google Scholar 

  15. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)

    CrossRef  Google Scholar 

  16. Prud’homme, C., Fages, J.-G., Lorca, X.: Choco Documentation. TASC - LS2N CNRS UMR 6241, COSLING S.A.S. (2017)

    Google Scholar 

  17. Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    MathSciNet  CrossRef  Google Scholar 

  18. Reiterer, S., Felfernig, A., Jeran, M., Stettinger, M., Wundara, M., Eixelsberger, W.: A wiki-based environment for constraint-based recommender systems applied in the e-government domain. In: Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on UMAP, Dublin, Ireland, 29 June–3 July 2015

    Google Scholar 

  19. Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011).

    CrossRef  MATH  Google Scholar 

  20. Tsang, E.P.K.: Foundations of Constraint Satisfaction. Computation in Cognitive Science. Academic Press, Cambridge (1993)

    Google Scholar 

  21. Von Winterfeldt, D.: Decision analysis and behavioral research (1986)

    Google Scholar 

  22. Wilson, R.D., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Int. Res. 6(1), 1–34 (1997)

    MathSciNet  MATH  Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Muesluem Atas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

  • eBook Packages: Computer ScienceComputer Science (R0)