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.
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- 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.
If this information is not provided, equal importance of all variables is assumed.
- 3.
Choco [16] is a free open-source constraint solver library for the Java programming language. http://www.choco-solver.org/.
- 4.
https://www.itu.dk/research/cla/externals/clib/, Maintained by CLA group. KB definition in CSP representation: https://github.com/CSPHeuristix/CDBC/.
- 5.
All user requirements were inconsistent with the underlying KB.
- 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.
For training and testing our approaches, we automatically generated again 500 user requirements. All user requirements were inconsistent with the underlying KB.
References
Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inf. Syst. 32(2000), 175–185 (2000)
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). https://doi.org/10.1007/978-3-642-23014-1_25
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)
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). https://doi.org/10.1007/978-3-642-02846-5_29
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). https://doi.org/10.1007/978-3-319-60042-0_39
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)
Gasparic, M., Janes, A.: What recommendation systems for software engineering recommend. J. Syst. Softw. 113(C), 101–113 (2016)
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)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction, 1st edn. Cambridge University Press, New York (2010)
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)
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)
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). https://doi.org/10.1007/3-540-45006-8_24
McSherry, D.: Maximally successful relaxations of unsuccessful queries. In: 15th Conference on AI and Cognitive Science, pp. 127–136. AAAI Press (2004)
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)
Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)
Prud’homme, C., Fages, J.-G., Lorca, X.: Choco Documentation. TASC - LS2N CNRS UMR 6241, COSLING S.A.S. (2017)
Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)
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
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). https://doi.org/10.1007/978-0-387-85820-3_1
Tsang, E.P.K.: Foundations of Constraint Satisfaction. Computation in Cognitive Science. Academic Press, Cambridge (1993)
Von Winterfeldt, D.: Decision analysis and behavioral research (1986)
Wilson, R.D., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Int. Res. 6(1), 1–34 (1997)
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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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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