Experimenting Analogical Reasoning in Recommendation

  • Nicolas Hug
  • Henri Prade
  • Gilles Richard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9384)


Recommender systems aim at providing suggestions of interest for end-users. Two main types of approach underlie existing recommender systems: content-based methods and collaborative filtering. In this paper, encouraged by good results obtained in classification by analogical proportion-based techniques, we investigate the possibility of using analogy as the main underlying principle for implementing a prediction algorithm of the collaborative filtering type. The quality of a recommender system can be estimated along diverse dimensions. The accuracy to predict user’s rating for unseen items is clearly an important matter. Still other dimensions like coverage and surprise are also of great interest. In this paper, we describe our implementation and we compare the proposed approach with well-known recommender systems.


  1. 1.
    Barbot, N., Miclet, L.: La proportion analogique dans les groupes. applications à l’apprentissage et à la génération. In: Proceedings Conference Francophone sur l’Apprentissage Artificiel (CAP), Hammamet, Tunisia (2009)Google Scholar
  2. 2.
    Bell, R.M., Koren, Y.: Lessons from the netflix prize challenge. SIGKDD Explor. Newsl. 9(2), 75–79 (2007)CrossRefGoogle Scholar
  3. 3.
    Correa Beltran, W., Jaudoin, H., Pivert, O.: Estimating null values in relational databases using analogical proportions. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014, Part III. CCIS, vol. 444, pp. 110–119. Springer, Heidelberg (2014) Google Scholar
  4. 4.
    Gentner, D., Holyoak, K.J., Kokinov, B.N.: The Analogical Mind: Perspectives from Cognitive Science. Cognitive Science, and Philosophy. MIT Press, Cambridge (2001) Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  6. 6.
    Kaminskas, M., Bridge, D.: Measuring surprise in recommender systems. In: Adamopoulos, P., et al. (ed.) Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (Workshop Programme of the 8th ACM Conference on Recommender Systems) (2014)Google Scholar
  7. 7.
    Lepage, Y.: De l’analogie rendant compte de la commutation en linguistique. Habilit. à Diriger des Recher., Univ. J. Fourier, Grenoble (2003)Google Scholar
  8. 8.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Olson, G.M., Jeffries, R., (eds.) Extended Abstracts Proceedings of the 2006 Conference on Human Factors in Computing Systems (CHI 2006), Montréal, Québec, Canada, 22–27 April, pp. 1097–1101 (2006)Google Scholar
  9. 9.
    Melis, E., Veloso, M.: Analogy in problem solving. In: Handbook of Practical Reasoning: Computational and Theoretical Aspects. Oxford University Press (1998)Google Scholar
  10. 10.
    Miclet, L., Prade, H.: Handling analogical proportions in classical logic and fuzzy logics settings. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 638–650. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  11. 11.
    Prade, H., Richard, G.: Analogical proportions and multiple-valued logics. In: van der Gaag, L.C. (ed.) ECSQARU 2013. LNCS, vol. 7958, pp. 497–509. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  12. 12.
    Prade, H., Richard, G.: From analogical proportion to logical proportions. Log. Univers. 7(4), 441–505 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Prade, H., Richard, G.: Homogenous and heterogeneous logical proportions. IfCoLog J. Log. Appl. 1(1), 1–51 (2014)Google Scholar
  14. 14.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Cambridge (2011) CrossRefzbMATHGoogle Scholar
  15. 15.
    Sakaguchi, T., Akaho, Y., Okada, K., Date, T., Takagi, T., Kamimaeda, N., Miyahara, M., Tsunoda, T.: Recommendation system with multi-dimensional and parallel-case four-term analogy. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC 2011), pp. 3137–3143 (2011)Google Scholar
  16. 16.
    Yvon, F., Stroppa, N.: Formal models of analogical proportions. Technical report D008, Ecole Nationale Supérieure des Télécommunications, Paris (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institut de Recherche en Informatique de ToulouseUniversité Paul SabatierToulouse Cedex 09France

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