Recommendations with Personality Traits Extracted from Text Reviews

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)


It is well known that human reasoning and decision-making are strongly influenced by psychological aspects. Recent works explore the adoption of personality traits to provide personalized recommendations. In this article, we report experimental results obtained with implicit recognition of Big Five personality traits from users’ text reviews. Hence, we present a personality-based recommender system with the analysis of the overall users’ satisfaction regarding the list of recommended items, showing promising results.



Special thanks to our participants for their cooperation.


  1. 1.
    Argamon, S., Dhawle, S., Koppel, M., Pennebaker, J.: Lexical predictors of personality type. In: Proceedings of the Joint Annual Meeting of the Interface and the Classification Society of North America, (2005)Google Scholar
  2. 2.
    Cantador, I., Fernandez-Tobas, I., Bellogn, A., Kosinski, M., Stillwell, D.: Relating personality types with user preferences in multiple entertainment domains. In: UMAP Workshops, Citeseer (2013)Google Scholar
  3. 3.
    Chen, J., Hsieh, G., Mahmud, J.U., Nichols, J.: Understanding individuals’ personal values from social media word use. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 405–414. ACM (2014)Google Scholar
  4. 4.
    Cremonesi, P., Garzotto, F., Turrin, R.: Investigating the persuasion potential of recommender systems from a quality perspective: an empirical study. ACM Trans. Interact. Intell. Syst. (TiiS) 2(2), 11 (2012)Google Scholar
  5. 5.
    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.) User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science, vol. 5535, pp. 259–270. Springer, Berlin (2009)CrossRefGoogle Scholar
  6. 6.
    Goldberg, L.R.: The development of markers for the big-five factor structure. Psychol. Assess. 4(1), 26 (1992)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, G., de la Rosa, J., Montaner, M., Delfin, S.: Embedding emotional context in recommender systems. In: IEEE 23rd International Conference on Data Engineering Workshop, pp. 845–852 (2007)Google Scholar
  8. 8.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  9. 9.
    Hu, R.: Design and user issues in personality-based recommender systems. In: Proceedings of the Fourth ACM Conference on Recommender Systems, vol. 10, pp. 357–360. ACM, New York, NY, USA (2010)Google Scholar
  10. 10.
    Hu, R., Pu, P.: A study on user perception of personality-based recommender systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science, vol. 6075, pp. 291–302. Springer, Berlin (2010)CrossRefGoogle Scholar
  11. 11.
    Iacobelli, F., Gill, A.J., Nowson, S., Oberlander, J.: Large scale personality classification of bloggers. In: Affective Computing and Intelligent Interaction, pp. 568–577. Springer (2011)Google Scholar
  12. 12.
    Lekakos, G., Giaglis, G.M.: Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors. Interact. Comput. 18(3), 410–431 (2006)CrossRefGoogle Scholar
  13. 13.
    Lin, C.H., McLeod, D., et al.: Exploiting and learning human temperaments for customized information recommendation. In: IMSA, pp. 218–223 (2002a)Google Scholar
  14. 14.
    Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30(1), 457–500 (2007)MATHGoogle Scholar
  15. 15.
    McCrae, R., Costa, P.: The Neo Personality Inventory Manual. Psychological Assessment Resources, Odessa (1985)Google Scholar
  16. 16.
    Norman, W.T.: Toward an adequate taxonomy of personality attributes: replicated factor structure in peer nomination personality ratings. J. Abnorm. Soc. Psychol. 66(6), 574 (1963)CrossRefGoogle Scholar
  17. 17.
    Nunes, M.A.S., Hu, R.: Personality-based recommender systems: an overview. In: Proceedings of the Sixth ACM Conference on Recommender Systems, p. 56. ACM (2012)Google Scholar
  18. 18.
    Oberlander, J., Nowson, S.: Whose thumb is it anyway?: classifying author personality from weblog text. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, Association for Computational Linguistics, pp. 627–634 (2006)Google Scholar
  19. 19.
    Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)CrossRefGoogle Scholar
  20. 20.
    Perik, E., De Ruyter, B., Markopoulos, P., Eggen, B.: The sensitivities of user profile information in music recommender systems. Proceedings of Private, Security, Trust, pp. 137–141 (2004)Google Scholar
  21. 21.
    Recio-Garcia, J.A., Jimenez-Diaz, G., Sanchez-Ruiz, A.A., Diaz-Agudo, B.: Personality aware recommendations to groups. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 325–328 ACM (2009)Google Scholar
  22. 22.
    Rentfrow, P.J., Gosling, S.D.: The do re mis of everyday life: the structure and personality correlates of music preferences. J. Personal. Soc. Psychol. 84(6), 1236 (2003)CrossRefGoogle Scholar
  23. 23.
    Roshchina, A., Cardiff, J., Rosso, P.: A comparative evaluation of personality estimation algorithms for the twin recommender system. In: Proceedings of the 3rd International Workshop on Search and Mining User-Generated Contents, pp. 11–18. ACM (2011)Google Scholar
  24. 24.
    Triandis, H.C., Suh, E.M.: Cultural influences on personality. Annu. Rev. Psychol. 53(1), 133–160 (2002)CrossRefGoogle Scholar
  25. 25.
    Zheng, N., Li, Q.: A recommender system based on tag and time information for social tagging systems. Expert Syst. Appl. 38(4), 45754587 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Politecnico di MilanoMilanoItaly

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