A Typicality-Based Recommendation Approach Leveraging Demographic Data

  • Aurélien Moreau
  • Olivier Pivert
  • Grégory Smits
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10333)


In this paper, we introduce a new recommendation approach leveraging demographic data. Items are associated with the audience who liked them, and we consider similarity based on audiences. More precisely, recommendations are computed on the basis of the (fuzzy) typical demographic properties (age, sex, occupation, etc.) of the audience associated with every item. Experiments on the MovieLens dataset show that our approach can find predictions that other tested state-of-the-art systems cannot.


Recommender systems Demographics Typicality Fuzzy logic 



This work has been partially funded by the French DGE (Direction Générale des Entreprises) under the project ODIN (Open Data INtelligence).


  1. 1.
    Bouchon-Meunier, B., Coletti, G., Lesot, M.-J., Rifqi, M.: Towards a conscious choice of a fuzzy similarity measure: a qualitative point of view. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS (LNAI), vol. 6178, pp. 1–10. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-14049-5_1 CrossRefGoogle Scholar
  2. 2.
    Cai, Y., Leung, H.F., Li, Q., Min, H., Tang, J., Li, J.: Typicality-based collaborative filtering recommendation. IEEE Trans. Knowl. Data Eng. 26(3), 766–779 (2014)CrossRefGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Weighted minimum and maximum operations in fuzzy set theory. Inf. Sci. 39, 205–210 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Funk, S.: Netflix update: try this at home (2006).
  5. 5.
    Jeckmans, A.J.P., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L., Tang, Q.: Privacy in recommender systems. In: Ramzan, N., van Zwol, R., Lee, J.-S., Clüver, K., Hua, X.-S. (eds.) Social Media Retrieval, pp. 263–281. Springer, London (2013)CrossRefGoogle Scholar
  6. 6.
    Krulwich, B.: LIFESTYLE FINDER: intelligent user profiling using large-scale demographic data. AI Mag. 18(2), 37 (1997)Google Scholar
  7. 7.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005, pp. 471–475 (2005)Google Scholar
  8. 8.
    Mcsherry, D.: Explanation in recommender systems. Artif. Intell. Rev. 24(2), 179–197 (2005)CrossRefzbMATHGoogle Scholar
  9. 9.
    Osherson, D., Smith, E.E.: On typicality and vagueness. Cognition 64(2), 189–206 (1997)CrossRefGoogle Scholar
  10. 10.
    Pappis, C., Karacapilidis, N.: A comparative assessment of measures of similarity of fuzzy values. Fuzzy Sets Syst. 56(2), 171–174 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)CrossRefGoogle Scholar
  12. 12.
    Pivert, O., Smits, G., Jaun, H.: Finding similar objects in relational databases - an association-based fuzzy approach. In: Flexible Query Answering Systems - 10th International Conference, FQAS 2013, Proceedings, pp. 425–436 (2013)Google Scholar
  13. 13.
    Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook, 2nd edn. Springer, Boston (2015)zbMATHGoogle Scholar
  14. 14.
    Vozalis, M.G., Margaritis, K.G.: Using SVD and demographic data for the enhancement of generalized Collaborative Filtering. Inf. Sci. 177(15), 3017–3037 (2007)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Chan, S.C.F., Ngai, G.: Applicability of demographic recommender system to tourist attractions: a case study on TripAdvisor. In: Proceeding of WI-IAT 2012, pp. 97–101 (2012)Google Scholar
  16. 16.
    Weinsberg, U., Bhagat, S., Ioannidis, S., Taft, N.: BlurMe: inferring and obfuscating user gender based on ratings. In: Proceedings of the 6th ACM Conference on Recommender Systems - RecSys 2012, pp. 195–202 (2012)Google Scholar
  17. 17.
    Yager, R.R.: A note on a fuzzy measure of typicality. Int. J. Intell. Syst. 12(3), 233–249 (1997)CrossRefzbMATHGoogle Scholar
  18. 18.
    Zadeh, L.: A computational theory of dispositions. Int. J. Intell. Syst. 2, 39–63 (1987)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aurélien Moreau
    • 1
  • Olivier Pivert
    • 1
  • Grégory Smits
    • 1
  1. 1.Irisa – University of Rennes 1, Technopole AnticipaLannion CedexFrance

Personalised recommendations