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Personality-Based Active Learning for Collaborative Filtering Recommender Systems

  • Mehdi Elahi
  • Matthias Braunhofer
  • Francesco Ricci
  • Marko Tkalcic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8249)

Abstract

Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user’s personality - using the Five Factor Model (FFM) - in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, context-aware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.

Keywords

Recommender System User Study Random Strategy Rating Request Active Learning Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Short personality quiz - psych central. Based upon the Ten-Item Personality Inventory (TIPI) (February 2013)Google Scholar
  2. 2.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16(5), 507–526 (2012)CrossRefGoogle Scholar
  3. 3.
    Costa, P., McCrae, R.: Toward a new generation of personality theories: Theoretical contexts for the five-factor model. In: The Five-Factor Model of Personality: Theoretical Perspectives, pp. 51–87 (1996)Google Scholar
  4. 4.
    Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating interface variants on personality acquisition for recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 259–270. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Elahi, M., Repsys, V., Ricci, F.: Rating elicitation strategies for collaborative filtering. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 160–171. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Elahi, M., Ricci, F., Rubens, N.: Adapting to natural rating acquisition with combined active learning strategies. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds.) ISMIS 2012. LNCS, vol. 7661, pp. 254–263. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Elahi, M., Ricci, F., Rubens, N.: Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective. ACM Transactions on Intelligent Systems and Technology 5(1) (2014)Google Scholar
  8. 8.
    Golbandi, N., Koren, Y., Lempel, R.: On bootstrapping recommender systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1805–1808. ACM (2010)Google Scholar
  9. 9.
    Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 595–604. ACM (2011)Google Scholar
  10. 10.
    Goldberg, L.R.: The development of markers for the big-five factor structure. Psychological Assessment 4(1), 26–42 (1992)CrossRefGoogle Scholar
  11. 11.
    Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the big-five personality domains. Journal of Research in Personality 37, 504–528 (2003)CrossRefGoogle Scholar
  12. 12.
    Hu, R., Pu, P.: A comparative user study on rating vs. personality quiz based preference elicitation methods. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI 2009, pp. 367–372. ACM, New York (2009)Google Scholar
  13. 13.
    Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 197–204. ACM, New York (2011)Google Scholar
  14. 14.
    John, O.P., Srivastava, S.: The big five trait taxonomy: History, measurement, and theoretical perspectives. In: Handbook of Personality: Theory and Research, vol. 2, pp. 102–138 (1999)Google Scholar
  15. 15.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  16. 16.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 2–5 (March 2013)Google Scholar
  17. 17.
    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., Mcnee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: Learning new user preferences in recommender systems. In: Proceedings of the 2002 International Conference on Intelligent User Interfaces, IUI 2002, pp. 127–134. ACM Press (2002)Google Scholar
  18. 18.
    Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter 10(2), 90–100 (2008)CrossRefGoogle Scholar
  19. 19.
    Rentfrow, P.J., Gosling, S.D., et al.: The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology 84(6), 1236–1256 (2003)CrossRefGoogle Scholar
  20. 20.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer (2011)Google Scholar
  21. 21.
    Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 735–767. Springer (2011)Google Scholar
  22. 22.
    Tkalcic, M., Kosir, A., Tasic, J.: The ldos-peraff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata. Journal on Multimodal User Interfaces 7(1-2), 143–155 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mehdi Elahi
    • 1
  • Matthias Braunhofer
    • 1
  • Francesco Ricci
    • 1
  • Marko Tkalcic
    • 2
  1. 1.Free University of Bozen-BolzanoBozen-BolzanoItaly
  2. 2.Johannes Kepler UniversityLinzAustria

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