Personality and Recommender Systems

  • Marko Tkalcic
  • Li Chen


Personality, as defined in psychology, accounts for the individual differences in users’ preferences and behaviour. It has been found that there are significant correlations between personality and users’ characteristics that are traditionally used by recommender systems (e.g. music preferences, social media behaviour, learning styles etc.). Among the many models of personality, the Five Factor Model (FFM) appears suitable for usage in recommender systems as it can be quantitatively measured (i.e. numerical values for each of the factors, namely, openness, conscientiousness, extraversion, agreeableness and neuroticism). The acquisition of the personality factors for an observed user can be done explicitly through questionnaires or implicitly using machine learning techniques with features extracted from social media streams or mobile phone call logs. There are, although limited, a number of available datasets to use in offline recommender systems experiment. Studies have shown that personality was successful at tackling the cold-start problem, making group recommendations, addressing cross-domain preferences and at generating diverse recommendations. However, a number of challenges still remain.


Recommender System Five Factor Model Personality Parameter Music Preference International Personality Item Pool 
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.



Part of the work presented in this chapter has received funding from the European Union FP7 programme through the PHENICX project (grant agreement no. 601166), China National Natural Science Foundation (no. 61272365), and Hong Kong Research Grants Council (no. ECS/HKBU211912).


  1. 1.
    Abbassi, Z., Mirrokni, V.S., Thakur, M.: Diversity maximization under matroid constraints. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13, pp. 32–40. ACM, New York, NY, USA (2013). DOI  10.1145/2487575.2487636
  2. 2.
    Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. Knowledge and Data Engineering, IEEE Transactions on 24(5), 896–911 (2012). DOI  10.1109/TKDE.2011.15 CrossRefGoogle Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, a.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005). DOI  10.1109/TKDE.2005.99
  4. 4.
    Amichai-Hamburger, Y., Vinitzky, G.: Social network use and personality. Computers in Human Behavior 26(6), 1289–1295 (2010)CrossRefGoogle Scholar
  5. 5.
    Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Science (New York, N.Y.) 337(6092), 337–41 (2012). DOI  10.1126/science.1215842
  6. 6.
    Augusta Silveira Netto Nunes, M., Santos Bezerra, J., Adicinéia, A.: PersonalityML: A Markup Language to Standardize the User Personality in Recommender Systems. Revista Gestão, Inovação e Tecnologia 2(3), 255–273 (2012). DOI  10.7198/S2237-0722201200030006
  7. 7.
    Bologna, C., Rosa, A.C.D., Vivo, A.D., Gaeta, M., Sansonetti, G., Viserta, V., A, Q.G.S.: Personality-Based Recommendation in E-Commerce. EMPIRE 2013: Emotions and Personality in Personalized Services (2013)Google Scholar
  8. 8.
    Braunhofer, M., Elahi, M., Ge, M., Ricci, F.: Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems. Learning and Collaboration Technologies. Technology-Rich Environments for Learning and Collaboration pp. 105–116 (2014). DOI  10.1007/978-3-319-07485-6_11
  9. 9.
    Cantador, I., Fernández-tobías, I., Bellogín, A.: Relating Personality Types with User Preferences in Multiple Entertainment Domains. EMPIRE 1st Workshop on “Emotions and Personality in Personalized Services”, 10. June 2013, Rome (2013)Google Scholar
  10. 10.
    Chen, L., Wu, W., He, L.: How personality influences users’ needs for recommendation diversity? CHI ’13 Extended Abstracts on Human Factors in Computing Systems on - CHI EA ’13 p. 829 (2013). DOI  10.1145/2468356.2468505
  11. 11.
    Chittaranjan, G., Blom, J., Gatica-Perez, D.: Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing 17(3), 433–450 (2011). DOI  10.1007/s00779-011-0490-1 CrossRefGoogle Scholar
  12. 12.
    Costa, P.T., Mccrae, R.R.: NEO PI-R professional manual. Odessa, FL (1992)Google Scholar
  13. 13.
    Deniz, M.: An Investigation of Decision Making Styles and the Five-Factor Personality Traits with Respect to Attachment Styles. Educational Sciences: Theory and Practice 11(1), 105–114 (2011)Google Scholar
  14. 14.
    Dennis, M., Masthoff, J., Mellish, C.: The quest for validated personality trait stories. In: Proceedings of the 2012 ACM international conference on Intelligent User Interfaces - IUI ’12, p. 273. ACM Press, New York, New York, USA (2012). DOI  10.1145/2166966.2167016
  15. 15.
    DeYoung, C.G., Quilty, L.C., Peterson, J.B.: Between facets and domains: 10 aspects of the Big Five. Journal of personality and social psychology 93(5), 880–896 (2007). DOI  10.1037/0022-3514.93.5.880 CrossRefGoogle Scholar
  16. 16.
    Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating interface variants on personality acquisition for recommender systems. User Modeling, Adaptation, and Personalization pp. 259–270 (2009). DOI  10.1007/978-3-642-02247-0_25
  17. 17.
    El-Bishouty, M.M., Chang, T.W., Graf, S., Chen, N.S.: Smart e-course recommender based on learning styles. Journal of Computers in Education 1(1), 99–111 (2014). DOI  10.1007/s40692-014-0003-0 CrossRefGoogle Scholar
  18. 18.
    Elahi, M., Braunhofer, M., Ricci, F., Tkalcic, M.: Personality-based active learning for collaborative filtering recommender systems. AI*IA 2013: Advances in Artificial Intelligence pp. 360–371 (2013). DOI  10.1007/978-3-319-03524-6_31
  19. 19.
    Elahi, M., Repsys, V., Ricci, F.: Rating elicitation strategies for collaborative filtering. E-Commerce and Web Technologies pp. 160–171 (2011)Google Scholar
  20. 20.
    Felder, R., Silverman, L.: Learning and teaching styles in engineering education. Engineering education 78(June), 674–681 (1988)Google Scholar
  21. 21.
    Gao, R., Hao, B., Bai, S., Li, L., Li, A., Zhu, T.: Improving user profile with personality traits predicted from social media content. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys ’13, pp. 355–358. ACM, New York, NY, USA (2013). DOI  10.1145/2507157.2507219
  22. 22.
    Golbeck, J., Robles, C., Turner, K.: Predicting personality with social media. Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems - CHI EA ’11 p. 253 (2011). DOI  10.1145/1979742.1979614
  23. 23.
    Goldberg, L., Johnson, J., Eber, H., Hogan, R., Ashton, M., Cloninger, C., Gough, H.: The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality 40(1), 84–96 (2006). DOI  10.1016/j.jrp.2005.08.007 CrossRefGoogle Scholar
  24. 24.
    Goldberg, L.R.: The Development of Markers for the Big-Five Factor Structure. Psychological assessment 4(1), 26–42 (1992)CrossRefGoogle Scholar
  25. 25.
    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(6), 504–528 (2003). DOI  10.1016/S0092-6566(03)00046-1 CrossRefGoogle Scholar
  26. 26.
    Hellriegel Don, Slocum, J.: Organizational Behavior. Cengage Learning (2010)Google Scholar
  27. 27.
    Holland, J.L.: Making vocational choices: A theory of vocational personalities and work environments. Psychological Assessment Resources (1997)Google Scholar
  28. 28.
    Hu, R., Pu, P.: A Study on User Perception of Personality-Based Recommender Systems. User Modeling, Adaptation, and Personalization 6075, 291–302 (2010). DOI  10.1007/978-3-642-13470-8_27 CrossRefGoogle Scholar
  29. 29.
    Hu, R., Pu, P.: Using Personality Information in Collaborative Filtering for New Users. Recommender Systems and the Social Web p. 17 (2010)Google Scholar
  30. 30.
    Hu, R., Pu, P.: Exploring Relations between Personality and User Rating Behaviors. EMPIRE 1st Workshop on “Emotions and Personality in Personalized Services”, 10. June 2013, Rome (2013)Google Scholar
  31. 31.
    Hurley, N., Zhang, M.: Novelty and diversity in top-n recommendation – analysis and evaluation. ACM Trans. Internet Technol. 10(4), 14:1–14:30 (2011). DOI  10.1145/1944339.1944341
  32. 32.
    Iacobelli, F., Gill, A.J., Nowson, S., Oberlander, J.: Large Scale Personality Classification of Bloggers. In: S. DMello, A. Graesser, B. Schuller, J.C. Martin (eds.) Affective Computing and Intelligent Interaction, Lecture Notes in Computer Science, vol. 6975, pp. 568–577. Springer Berlin Heidelberg, Berlin, Heidelberg (2011). DOI  10.1007/978-3-642-24571-8 CrossRefGoogle Scholar
  33. 33.
    John, O.P., Srivastava, S.: The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In: L.A. Pervin, O.P. John (eds.) Handbook of personality: Theory and research, vol. 2, second edn., pp. 102–138. Guilford Press, New York (1999)Google Scholar
  34. 34.
    Keirsey, D.: Please Understand Me 2? Prometheus Nemesis pp. 1–350 (1998)Google Scholar
  35. 35.
    Kompan, M., Bieliková, M.: Social Structure and Personality Enhanced Group Recommendation. UMAP 2014 Extended Proceedings (2014)Google Scholar
  36. 36.
    Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. Computer 42(8), 30–37 (2009). DOI  10.1109/MC.2009.263 CrossRefGoogle Scholar
  37. 37.
    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 pp. 2–5 (2013). DOI  10.1073/pnas.1218772110
  38. 38.
    Košir, A., Odić, A., Kunaver, M., Tkalčič, M., Tasič, J.F.: Database for contextual personalization. Elektrotehniški vestnik 78(5), 270–274 (2011)Google Scholar
  39. 39.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8. Tech. rep., University of Florida (2005)Google Scholar
  40. 40.
    van Lankveld, G., Spronck, P., van den Herik, J., Arntz, A.: Games as personality profiling tools. 2011 IEEE Conference on Computational Intelligence and Games (CIG’11) pp. 197–202 (2011). DOI  10.1109/CIG.2011.6032007
  41. 41.
    Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Modeling and User-Adapted Interaction: The Journal of Personalization Research 16(3-4), 281–319 (2006). DOI  10.1007/s11257-006-9008-3 CrossRefGoogle Scholar
  42. 42.
    McCrae, R., Allik, I.: The five-factor model of personality across cultures. Springer (2002)Google Scholar
  43. 43.
    McCrae, R.R., Costa, P.T.: A contemplated revision of the NEO Five-Factor Inventory. Personality and Individual Differences 36(3), 587–596 (2004). DOI  10.1016/S0191-8869(03)00118-1 CrossRefGoogle Scholar
  44. 44.
    McCrae, R.R., John, O.P.: An Introduction to the Five-Factor Model and its Applications. Journal of Personality 60(2), p175–215 (1992)CrossRefGoogle Scholar
  45. 45.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough. In: CHI ’06 extended abstracts on Human factors in computing systems - CHI EA ’06, p. 1097. ACM Press, New York, New York, USA (2006). DOI  10.1145/1125451.1125659
  46. 46.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: CHI ’06 Extended Abstracts on Human Factors in Computing Systems, CHI EA ’06, pp. 1097–1101. ACM, New York, NY, USA (2006). DOI  10.1145/1125451.1125659
  47. 47.
    Nowson, S., Oberlander, J.: Identifying more bloggers: Towards large scale personality classification of personal weblogs. International Conference on Weblogs and Social Media. (2007)Google Scholar
  48. 48.
    Nunes, M.A.S., Hu, R.: Personality-based recommender systems. In: Proceedings of the sixth ACM conference on Recommender systems - RecSys ’12, p. 5. ACM Press, New York, New York, USA (2012). DOI  10.1145/2365952.2365957
  49. 49.
    Nunes, M.A.S.N.: Recommender Systems based on Personality Traits: Could human psychological aspects influence the computer decision-making process? VDM Verlag (2009)Google Scholar
  50. 50.
    Odić, A., Tkalčič, M., Tasic, J.F., Košir, A.: Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System. Interacting with Computers 25(1), 74–90 (2013). DOI  10.1093/iwc/iws003 Google Scholar
  51. 51.
    Odić, A., Tkalčič, M., Tasič, J.F., Košir, A.: Personality and Social Context: Impact on Emotion Induction from Movies. UMAP 2013 Extended Proceedings (2013)Google Scholar
  52. 52.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates p. 71 (2001)Google Scholar
  53. 53.
    Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. In: 2011 IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing, pp. 180–185. IEEE (2011). DOI  10.1109/PASSAT/SocialCom.2011.26
  54. 54.
    Quijano-Sanchez, L., Recio-Garcia, J.a., Diaz-Agudo, B.: Personality and Social Trust in Group Recommendations. 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (c), 121–126 (2010). DOI  10.1109/ICTAI.2010.92
  55. 55.
    Rawlings, D., Ciancarelli, V.: Music Preference and the Five-Factor Model of the NEO Personality Inventory. Psychology of Music 25(2), 120–132 (1997). DOI  10.1177/0305735697252003 CrossRefGoogle Scholar
  56. 56.
    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 - RecSys ’09, p. 325. ACM Press, New York, New York, USA (2009). DOI  10.1145/1639714.1639779
  57. 57.
    Rentfrow, P.J., Goldberg, L.R., Zilca, R.: Listening, watching, and reading: the structure and correlates of entertainment preferences. Journal of personality 79(2), 223–58 (2011). DOI  10.1111/j.1467-6494.2010.00662.x CrossRefGoogle Scholar
  58. 58.
    Rentfrow, P.J., Gosling, S.D.: 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). DOI  10.1037/0022-3514.84.6.1236 CrossRefGoogle Scholar
  59. 59.
    Ross, C., Orr, E.S., Sisic, M., Arseneault, J.M., Simmering, M.G., Orr, R.R.: Personality and motivations associated with facebook use. Computers in Human Behavior 25(2), 578–586 (2009)CrossRefGoogle Scholar
  60. 60.
    Schrammel, J., Köffel, C., Tscheligi, M.: Personality traits, usage patterns and information disclosure in online communities. Proceedings of the 23rd British HCI …pp. 169–174 (2009)Google Scholar
  61. 61.
    Selfhout, M., Burk, W., Branje, S., Denissen, J., van Aken, M., Meeus, W.: Emerging late adolescent friendship networks and Big Five personality traits: a social network approach. Journal of personality 78(2), 509–38 (2010). DOI  10.1111/j.1467-6494.2010.00625.x CrossRefGoogle Scholar
  62. 62.
    Sha, X., Quercia, D., Michiardi, P., Dell’Amico, M.: Spotting trends. In: Proceedings of the sixth ACM conference on Recommender systems - RecSys ’12, p. 51. ACM Press, New York, New York, USA (2012). DOI  10.1145/2365952.2365967
  63. 63.
    Shen, J., Brdiczka, O., Liu, J.: Understanding Email Writers: Personality Prediction from Email Messages. User Modeling, Adaptation, and Personalization pp. 318–330 (2013). DOI  10.1007/978-3-642-38844-6_29
  64. 64.
    Soloman, B.A., Felder, R.M.: Index of Learning Styles Questionnaire (2014). URL
  65. 65.
    Stewart, B.: Personality And Play Styles: A Unified Model (2011)Google Scholar
  66. 66.
    Thomas, K.W.: Conflict and conflict management: Reflections and update. Journal of Organizational Behavior 13(3), 265–274 (1992). DOI  10.1002/job.4030130307 CrossRefGoogle Scholar
  67. 67.
    Tintarev, N., Dennis, M., Masthoff, J.: Adapting Recommendation Diversity to Openness to Experience: A Study of Human Behaviour. User Modeling, Adaptation, and Personalization, Lecture Notes in Computer Science Volume 7899 (I), 190–202 (2013). DOI  10.1007/978-3-642-38844-6_16 CrossRefGoogle Scholar
  68. 68.
    Tiroshi, A., Kuflik, T.: Domain ranking for cross domain collaborative filtering. User Modeling, Adaptation, and Personalization pp. 328–333 (2012). DOI  10.1007/978-3-642-31454-4_30
  69. 69.
    Tkalcic, M., Kunaver, M., Košir, A., Tasic, J.: Addressing the new user problem with a personality based user similarity measure. Joint Proceedings of the Workshop on Decision Making and Recommendation Acceptance Issues in Recommender Systems (DEMRA 2011) and the 2nd Workshop on User Models for Motivational Systems: The affective and the rational routes to persuasion (UMMS 2011) (2011)Google Scholar
  70. 70.
    Tkalčič, M., Burnik, U., Košir, A.: Using affective parameters in a content-based recommender system for images. User Modeling and User-Adapted Interaction 20(4), 279–311 (2010). DOI  10.1007/s11257-010-9079-z CrossRefGoogle Scholar
  71. 71.
    Tkalčič, M., Košir, A., Tasič, 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). DOI  10.1007/s12193-012-0107-7 CrossRefGoogle Scholar
  72. 72.
    Tkalčič, M., Kunaver, M., Tasič, J., Košir, A.: Personality Based User Similarity Measure for a Collaborative Recommender System. 5th Workshop on Emotion in Human-Computer Interaction-Real World Challenges p. 30 (2009)Google Scholar
  73. 73.
    Winoto, P., Tang, T.: If You Like the Devil Wears Prada the Book, Will You also Enjoy the Devil Wears Prada the Movie? A Study of Cross-Domain Recommendations. New Generation Computing 26(3), 209–225 (2008). DOI  10.1007/s00354-008-0041-0 CrossRefGoogle Scholar
  74. 74.
    Wu, W., Chen, L., He, L.: Using personality to adjust diversity in recommender systems. Proceedings of the 24th ACM Conference on Hypertext and Social Media - HT ’13 (May), 225–229 (2013). DOI  10.1145/2481492.2481521
  75. 75.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, WWW ’05, pp. 22–32. ACM, New York, NY, USA (2005). DOI  10.1145/1060745.1060754

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Authors and Affiliations

  1. 1.Johannes Kepler UniversityLinzAustria
  2. 2.Hong Kong Baptist UniversityKowloon Tong, KowloonChina

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