Learning User Models in Multi-criteria Recommender Systems

  • Marilena Agathokleous
  • Nicolas Tsapatsoulis
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)


Whenever people have to choose seeing or buying an item among many others, they are based on their own ways of evaluating its characteristics (criteria) to understand better which one of the items meets their needs. Based on this argument, in this paper we develop personalized models for each user, according to their ratings on specific criteria, and we use them in multi-criteria recommender systems. We assume the overall ranking, which indicates users’ final decision, is closely related to their given value in each criterion separately. We compare user models created using neural networks and linear regression and we show, as expected from the implicit nonlinear combination of criteria, that neural networks based models achieve better performance. In continue we investigate several different approaches of collaborative filtering and matrix factorization to make recommendations. For this purpose we estimate users’ similarity by comparing their models. Experimental justification is obtained using the Yahoo! Movie dataset.


User modeling Multi-criteria recommender systems Collaborative filtering MCDA Matrix factorization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adomavicius, G., Kwon, Y.: New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems, 48–55 (2007)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–250. Springer US (2011)Google Scholar
  3. 3.
    Agathokleous, M., Tsapatsoulis, N.: Voting Advice Applications: Missing Value Estimation Using Matrix Factorization and Collaborative Filtering. In: Papadopoulos, H., Andreou, A.S., Iliadis, L., Maglogiannis, I. (eds.) AIAI 2013. IFIP AICT, vol. 412, pp. 20–29. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Bruine de Bruin, W., Parker, A., Fischhoff, B.: Individual Differences in Adult Decision-Making Competence (A-DMC). Journal of Personality and Social Psychology 92, 938–956 (2007)CrossRefGoogle Scholar
  5. 5.
    Dodgson, J.S., Spackman, M., Pearman, A., Phillips, L.D.: Multi-criteria analysis: a manual. Department for Communities and Local Government: London (2009) ISBN 9781409810230Google Scholar
  6. 6.
    Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis, 7th edn. Prentice Hall (2010)Google Scholar
  7. 7.
    Jannach, D., Gedikli, F., Karakaya, Z., Juwig, O.: Recommending hotels based on multi-dimensional customer ratings. In: International Conference on Information and Communication Technologies in Tourism, pp. 320–331. Springer (2012)Google Scholar
  8. 8.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010)Google Scholar
  9. 9.
    Jullisson, E.A., Karlsson, N., Garling, T.: Weighing the past and the future in decision making. European Journal of Cognitive Psychology 17(4), 561–575 (2005), doi:10.1080/09541440440000159.CrossRefGoogle Scholar
  10. 10.
    Herlocker, J.L., Konstan, J.A., Riedl, J.T.: An empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5(4), 287–310 (2002)CrossRefGoogle Scholar
  11. 11.
    Reason, J.: Human error. Cambridge University Press, New York (1990)CrossRefGoogle Scholar
  12. 12.
    Salakhutdinov, R., Mnih, A.: Probabilistic Matrix Factorization. In: Advances in Neural Information Processing Systems (NIPS 2007), pp. 1257–1264. ACM Press (2008)Google Scholar
  13. 13.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating Word of mouth. In: ACM CHI 1995 Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press (1995)Google Scholar
  14. 14.
    Taner, M.T.: Neural networks and computation of neural network weights and biases by the generalized delta rule and back-propagation of errors (1995)Google Scholar
  15. 15.
    Tsapatsoulis, N., Georgiou, O.: Investigating the Scalability of Algorithms, the Role of Similarity Metric and the List of Suggested Items Construction Scheme in Recommender Systems. International Journal on Artificial Intelligence Tools 21(4), 19–26 (2012)Google Scholar
  16. 16.
    West, R.F., Meserve, R.J., Stanovich, K.E.: Cognitive Sophistication Does Not Attenuate the Bias Blind Spot. Journal of Personality and Social Psychology (2012), doi:10.1037/a0028857 (Advance online publication)Google Scholar
  17. 17.
    Zhou, T., Shan, H., Banerjee, A., Sapiro, G.: Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information. In: SIAM International Conference on Data Mining, pp. 403–414. SIAM / Omnipress (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marilena Agathokleous
    • 1
  • Nicolas Tsapatsoulis
    • 1
  1. 1.LimassolCyprus

Personalised recommendations