Skip to main content
SpringerLink
Log in

Personality and Recommender Systems

  • Chapter
  • First Online:
Recommender Systems Handbook

Abstract

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 generating diverse recommendations. However, a number of challenges still remain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The Thomas-Kilmann conflict mode instrument is available at http://cmpresolutions.co.uk/wp-content/uploads/2011/04/Thomas-Kilman-conflict-instrument-questionaire.pdf.

  2. 2.

    https://www.douban.com/group/explore.

  3. 3.

    http://ipip.ori.org/.

  4. 4.

    http://markotkalcic.com/resources.html.

  5. 5.

    https://www.lucami.org/en/research/ldos-comoda-dataset/.

  6. 6.

    https://sites.google.com/michalkosinski.com/mypersonality (the dataset was stopped sharing in 2018).

  7. 7.

    The dataset is not publicly available anymore.

  8. 8.

    https://www.comp.hkbu.edu.hk/~lichen/download/TaoBao_Serendipity_Dataset.html.

  9. 9.

    Here we mainly consider the so called “intra-list diversity” within a set of recommended items (see Chapter “Novelty and Diversity in Recommender Systems”).

  10. 10.

    https://grouplens.org/datasets/movielens/.

References

  1. G. Adomavicius, Y. Kwon, Toward more diverse recommendations: item re-ranking methods for recommender systems, in Workshop on Information Technologies and Systems (WITS 2009). Citeseer (2009), pp. 417–440

    Google Scholar 

  2. G. Adomavicius, Y. Kwon, Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012). https://doi.org/10.1109/TKDE.2011.15

    Article  Google Scholar 

  3. G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  4. Y. Amichai-Hamburger, G. Vinitzky, Social network use and personality. Comput. Human Behav. 26(6), 1289–1295 (2010)

    Article  Google Scholar 

  5. S. Aral, D. Walker, Identifying influential and susceptible members of social networks. Science (New York, N.Y.) 337(6092), 337–341 (2012). https://doi.org/10.1126/science.1215842

  6. M.A.S.N. Nunes, J. Santos Bezerra, A. Adicinéia, PersonalityML: a markup language to standardize the user personality in recommender systems. Rev. Gestão Inovação e Tecnol. 2(3), 255–273 (2012). https://doi.org/10.7198/S2237-0722201200030006

    Article  Google Scholar 

  7. D. Azucar, D. Marengo, M. Settanni, Predicting the big 5 personality traits from digital footprints on social media: a meta-analysis. Pers. Individ. Dif. 124, 150–159 (2018). https://doi.org/10.1016/j.paid.2017.12.018. http://www.sciencedirect.com/science/article/pii/S0191886917307328

  8. S. Berkovsky, R. Taib, I. Koprinska, E. Wang, Y. Zeng, J. Li, S. Kleitman, Detecting personality traits using eye-tracking data, in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19 (2019), pp. 1–12. https://doi.org/10.1145/3290605.3300451. http://dl.acm.org/citation.cfm?doid=3290605.3300451

  9. D.E. Berlyne, Conflict, Arousal and Curiosity (McGraw-Hill, New York, 1960)

    Book  Google Scholar 

  10. C. Bologna, A.C.D. Rosa, A.D. Vivo, M. Gaeta, G. Sansonetti, V. Viserta, Personality-based recommendation in E-commerce, in EMPIRE 2013: Emotions and Personality in Personalized Services (2013)

    Google Scholar 

  11. M.M. Bradley, P.J. Lang, The International Affective Picture System (IAPS) in the study of emotion and attention, in Handbook of Emotion Elicitation and Assessment, Series in Affective Science, ed. by J.A. Coan, J.J. Allen, Chap. 2 (Oxford University Press, 2007), pp. 29–46. http://books.google.com/books?hl=en&lr=&id=ChiiBDGyewoC&oi=fnd&pg=PA29&dq =The+international+affective+picture+system+(IAPS)+in+the+study+of+emotion+and+att ention&ots=pJyOP0Y8rD&sig=VJXcIRILIEtevfO38sLZ3rHCNT8%5Cnhttp://books.google .com/books?hl=en&lr=&id=Ch

    Google Scholar 

  12. M. Braunhofer, M. Elahi, M. Ge, F. Ricci, Context dependent preference acquisition with personality-based active learning in mobile recommender systems, in Learning and Collaboration Technologies. Technology-Rich Environments for Learning and Collaboration (2014), pp. 105–116. https://doi.org/10.1007/978-3-319-07485-6_11

  13. B. Brost, R. Mehrotra, T. Jehan, The music streaming sessions dataset, in The World Wide Web Conference, WWW ’19 (Association for Computing Machinery, New York, 2019), pp. 2594–2600. https://doi.org/10.1145/3308558.3313641

    Book  Google Scholar 

  14. I. Cantador, I. Fernández-tobías, A. Bellogín, Relating personality types with user preferences in multiple entertainment domains, in EMPIRE 1st Workshop on “Emotions and Personality in Personalized Services”, Rome, 10 June 2013

    Google Scholar 

  15. L. Chen, W. Wu, L. He, How personality influences users’ needs for recommendation diversity? in CHI ’13 Extended Abstracts on Human Factors in Computing Systems on - CHI EA ’13 (2013), p. 829. https://doi.org/10.1145/2468356.2468505

  16. L. Chen, Y. Yang, N. Wang, K. Yang, Q. Yuan, How serendipity improves user satisfaction with recommendations? A large-scale user evaluation, in The World Wide Web Conference, WWW ’19 (Association for Computing Machinery, New York, 2019), pp. 240–250. https://doi.org/10.1145/3308558.3313469

    Google Scholar 

  17. G. Chittaranjan, J. Blom, D. Gatica-Perez, Mining large-scale smartphone data for personality studies. Pers. Ubiquitous Comput. 17(3), 433–450 (2011). https://doi.org/10.1007/s00779-011-0490-1

    Article  Google Scholar 

  18. C.L. Clarke, M. Kolla, G.V. Cormack, O. Vechtomova, A. Ashkan, S. Büttcher, I. MacKinnon, Novelty and diversity in information retrieval evaluation, in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008) (ACM, New York, 2008), pp. 659–666

    Google Scholar 

  19. P.T. Costa, R.R. Mccrae, NEO PI-R professional manual, Odessa, FL (1992)

    Google Scholar 

  20. A. Delic, J. Neidhardt, T.N. Nguyen, F. Ricci, An observational user study for group recommender systems in the tourism domain. Inf. Technol. Tour. (2018). https://doi.org/10.1007/s40558-018-0106-y. http://link.springer.com/10.1007/s40558-018-0106-y

  21. A. Delić, T.N. Nguyen, M. Tkalčič, Group decision-making and designing group recommender systems, in Handbook of e-Tourism (Springer International Publishing, Cham, 2020), pp. 1–23. https://doi.org/10.1007/978-3-030-05324-6_57-1. http://link.springer.com/10.1007/978-3-030-05324-6_57-1

  22. M. Deniz, An investigation of decision making styles and the five-factor personality traits with respect to attachment styles. Educ. Sci. Theory Pract. 11(1), 105–114 (2011)

    Google Scholar 

  23. M. Dennis, J. Masthoff, C. Mellish, The quest for validated personality trait stories, in Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces - IUI ’12 (ACM Press, New York, 2012). https://doi.org/10.1145/2166966.2167016

    Google Scholar 

  24. C.G. DeYoung, L.C. Quilty, J.B. Peterson, Between facets and domains: 10 aspects of the Big Five. J. Pers. Soc. Psychol. 93(5), 880–896 (2007). https://doi.org/10.1037/0022-3514.93.5.880

    Article  Google Scholar 

  25. G. Dunn, J. Wiersema, J. Ham, L. Aroyo, Evaluating interface variants on personality acquisition for recommender systems, in User Modeling, Adaptation, and Personalization (2009), pp. 259–270. https://doi.org/10.1007/978-3-642-02247-0_25

  26. M.M. El-Bishouty, T.W. Chang, S. Graf, N.S. Chen, Smart e-course recommender based on learning styles. J. Comput. Educ. 1(1), 99–111 (2014). https://doi.org/10.1007/s40692-014-0003-0

    Article  Google Scholar 

  27. M. Elahi, M. Braunhofer, F. Ricci, M. Tkalcic, Personality-based active learning for collaborative filtering recommender systems, in AI*IA 2013: Advances in Artificial Intelligence (2013), pp. 360–371. https://doi.org/10.1007/978-3-319-03524-6_31

  28. M. Elahi, V. Repsys, F. Ricci, Rating elicitation strategies for collaborative filtering, in E-Commerce and Web Technologies (2011), pp. 160–171

    Google Scholar 

  29. F. Eskandanian, B. Mobasher, R. Burke, A clustering approach for personalizing diversity in collaborative recommender systems, in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP 2017) (ACM, New York, 2017), pp. 280–284

    Book  Google Scholar 

  30. R. Felder, L. Silverman, Learning and teaching styles in engineering education. Eng. Educ. 78(June), 674–681 (1988)

    Google Scholar 

  31. I. Fernández-Tobías, M. Braunhofer, M. Elahi, F. Ricci, I. Cantador, Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User-Adapt. Interact. 26(2–3), 221–255 (2016). https://doi.org/10.1007/s11257-016-9172-z.

    Article  Google Scholar 

  32. B. Ferwerda, M. Schedl, M. Tkalcic, Predicting personality traits with instagram pictures, in Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015 - EMPIRE ’15, ed. by M. Tkalčič, B. De Carolis, M. de Gemmis, A. Odić, A. Košir (ACM Press, New York, 2015), pp. 7–10. https://doi.org/10.1145/2809643.2809644. http://dl.acm.org/citation.cfm?doid=2809643.2809644

  33. B. Ferwerda, M. Schedl, M. Tkalcic, Personality traits and the relationship with (non-) disclosure behavior on Facebook, in Proceedings of the 25th International Conference Companion on World Wide Web - WWW ’16 Companion (ACM Press, New York, 2016), pp. 565–568. https://doi.org/10.1145/2872518.2890085

    Book  Google Scholar 

  34. B. Ferwerda, M. Tkalcic, Predicting users’ personality from instagram pictures, in Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization - UMAP ’18 (ACM Press, New York, 2018), pp. 157–161. https://doi.org/10.1145/3209219.3209248. http://dl.acm.org/citation.cfm?doid=3209219.3209248

  35. B. Ferwerda, M. Tkalčič, Exploring the prediction of personality traits from drug consumption profiles, in Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’20 Adjunct (Association for Computing Machinery, New York, 2020), pp. 2–5. https://doi.org/10.1145/3386392.3397589

    Google Scholar 

  36. R. Gao, B. Hao, S. Bai, L. Li, A. Li, T. Zhu, Improving user profile with personality traits predicted from social media content, in Proceedings of the 7th ACM Conference on Recommender Systems, RecSys ’13 (ACM, New York, 2013), pp. 355–358. https://doi.org/10.1145/2507157.2507219

    Google Scholar 

  37. J. Golbeck, C. Robles, K. Turner, Predicting personality with social media, in Proceedings of the 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA ’11 (2011), p. 253. https://doi.org/10.1145/1979742.1979614

  38. L. Goldberg, J. Johnson, H. Eber, R. Hogan, M. Ashton, C. Cloninger, H. Gough, The international personality item pool and the future of public-domain personality measures. J. Res. Personal. 40(1), 84–96 (2006). https://doi.org/10.1016/j.jrp.2005.08.007

    Article  Google Scholar 

  39. L.R. Goldberg, The development of markers for the big-five factor structure. Psychol. Assess. 4(1), 26–42 (1992)

    Article  Google Scholar 

  40. S.D. Gosling, P.J. Rentfrow, W.B. Swann, A very brief measure of the Big-Five personality domains. J. Res. Personal. 37(6), 504–528 (2003). https://doi.org/10.1016/S0092-6566(03)00046-1. http://linkinghub.elsevier.com/retrieve/pii/S0092656603000461

  41. D. Hellriegel, J. Slocum, Organizational Behavior (Cengage Learning, New York, 2010)

    Google Scholar 

  42. J.L. Holland, Making Vocational Choices: A Theory of Vocational Personalities and Work Environments (Psychological Assessment Resources, Washington, DC, 1997)

    Google Scholar 

  43. R. Hu, P. Pu, A study on user perception of personality-based recommender systems. User Model. Adapt. Personal. 6075, 291–302 (2010). https://doi.org/10.1007/978-3-642-13470-8_27

    Article  Google Scholar 

  44. R. Hu, P. Pu, Using personality information in collaborative filtering for new users, in Recommender Systems and the Social Web (2010), p. 17

    Google Scholar 

  45. R. Hu, P. Pu, Exploring relations between personality and user rating behaviors, in EMPIRE 1st Workshop on “Emotions and Personality in Personalized Services”, Rome 10 June 2013

    Google Scholar 

  46. N. Hurley, M. Zhang, Novelty and diversity in top-n recommendation – analysis and evaluation. ACM Trans. Internet Technol. 10(4), 14:1–14:30 (2011). https://doi.org/10.1145/1944339.1944341

  47. F. Iacobelli, A.J. Gill, S. Nowson, J. Oberlander, Large scale personality classification of bloggers, in Affective Computing and Intelligent Interaction, ed. by S. D’Mello, A. Graesser, B. Schuller, J.C. Martin. Lecture Notes in Computer Science, vol. 6975 (Springer, Berlin, 2011), pp. 568–577. https://doi.org/10.1007/978-3-642-24571-8

  48. O.P. John, S. Srivastava, The Big Five trait taxonomy: history, measurement, and theoretical perspectives, in Handbook of Personality: Theory and Research, vol. 2, 2nd edn. ed. by L.A. Pervin, O.P. John (Guilford Press, New York, 1999), pp. 102–138

    Google Scholar 

  49. T.B. Kashdan, M.W. Gallagher, P.J. Silvia, B.P. Winterstein, W.E. Breen, D. Terhar, M.F. Steger, The curiosity and exploration inventory-II: development, factor structure, and psychometrics. J. Res. Personal. 43(6), 987–998 (2009). https://doi.org/10.1016/j.jrp.2009.04.011

    Article  Google Scholar 

  50. D. Keirsey, Please Understand Me 2? (Prometheus Nemesis, Del Mar, 1998), pp. 1–350

    Google Scholar 

  51. M. Khwaja, M. Ferrer, J.O. Iglesias, A. Aldo Faisal, A. Matic, Aligning daily activities with personality: towards a recommender system for improving wellbeing, in RecSys 2019 - 13th ACM Conference on Recommender Systems (Section 3) (2019), pp. 368–372. https://doi.org/10.1145/3298689.3347020

  52. M. Kompan, M. Bieliková, Social structure and personality enhanced group recommendation, in UMAP 2014 Extended Proceedings (2014)

    Google Scholar 

  53. M. Kosinski, D. Stillwell, T. Graepel, Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 2–5 (2013). https://doi.org/10.1073/pnas.1218772110

  54. A. Košir, A. Odić, M. Kunaver, M. Tkalčič, J.F. Tasič, Database for contextual personalization. Elektrotehniški vestnik 78(5), 270–274 (2011)

    Google Scholar 

  55. A.D.I. Kramer, J.E. Guillory, J.T. Hancock, Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. USA 111(29), 8788–90 (2014). https://doi.org/10.1073/pnas.1320040111. http://www.ncbi.nlm.nih.gov/pubmed/24994898 http://www.ncbi.nlm.nih.gov/pubmed/24889601

  56. P.J. Lang, M.M. Bradley, B.N. Cuthbert, International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report A-8. Tech. rep., University of Florida, 2005

    Google Scholar 

  57. G. van Lankveld, P. Spronck, J. van den Herik, A. Arntz, Games as personality profiling tools, in 2011 IEEE Conference on Computational Intelligence and Games (CIG’11) (2011), pp. 197–202. https://doi.org/10.1109/CIG.2011.6032007

  58. S. Manolios, A. Hanjalic, C.C.S. Liem, The influence of personal values on music taste, in Proceedings of the 13th ACM Conference on Recommender Systems (ACM, New York, 2019), pp. 501–505. https://doi.org/10.1145/3298689.3347021. https://dl.acm.org/doi/10.1145/3298689.3347021

  59. J. Masthoff, A. Gatt, In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User-Adapt. Interact. J. Personal. Res. 16(3–4), 281–319 (2006). https://doi.org/10.1007/s11257-006-9008-3

    Article  Google Scholar 

  60. C. Matt, T. Hess, A. Benlian, C. Weiß, Escaping from the filter bubble? The effects of novelty and serendipity on users’ evaluations of online recommendations (2014). https://EconPapers.repec.org/RePEc:dar:wpaper:66193

  61. R. McCrae, I. Allik, The Five-Factor Model of Personality Across Cultures (Springer, Berlin, 2002)

    Book  Google Scholar 

  62. R.R. McCrae, P.T. Costa, A contemplated revision of the NEO Five-Factor Inventory. Pers. Individ. Dif. 36(3), 587–596 (2004). https://doi.org/10.1016/S0191-8869(03)00118-1

    Article  Google Scholar 

  63. R.R. McCrae, O.P. John, An introduction to the five-factor model and its applications. J. Personal. 60(2), 175–215 (1992)

    Article  Google Scholar 

  64. S.M. McNee, J. Riedl, J.A. Konstan, 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 (ACM, New York, 2006), pp. 1097–1101. https://doi.org/10.1145/1125451.1125659

    Google Scholar 

  65. A.B. Melchiorre, M. Schedl, Personality correlates of music audio preferences for modelling music listeners, in Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’20 (Association for Computing Machinery, New York, 2020), pp. 313–317. https://doi.org/10.1145/3340631.3394874

    Google Scholar 

  66. T.N. Nguyen, F. Ricci, Situation-dependent combination of long-term and session-based preferences in group recommendations: an experimental analysis, in Proceedings of Sac (2018), pp. 1366–1373. https://doi.org/10.1145/3167132.3167279

  67. T.N. Nguyen, F. Ricci, A. Delic, D. Bridge, Conflict resolution in group decision making: insights from a simulation study. User Model. User-Adapt. Interact. 29(5), 895–941 (2019)

    Article  Google Scholar 

  68. S. Nowson, J. Oberlander, Identifying more bloggers: towards large scale personality classification of personal weblogs, in International Conference on Weblogs and Social Media (2007)

    Google Scholar 

  69. M.A.S. Nunes, R. Hu, Personality-based recommender systems, in Proceedings of the Sixth ACM Conference on Recommender Systems - RecSys ’12 (ACM Press, New York, 2012), p. 5. https://doi.org/10.1145/2365952.2365957

    Book  Google Scholar 

  70. M.A.S.N. Nunes, Recommender Systems Based on Personality Traits: Could Human Psychological Aspects Influence the Computer Decision-Making Process? (VDM Verlag, Berlin, 2009)

    Google Scholar 

  71. A. Odić, M. Tkalčič, J.F. Tasic, A. Košir, Predicting and detecting the relevant contextual information in a movie-recommender system. Interact. Comput. 25(1), 74–90 (2013). https://doi.org/10.1093/iwc/iws003

    Article  Google Scholar 

  72. A. Odić, M. Tkalčič, J.F. Tasič, A. Košir, Personality and social context : impact on emotion induction from movies, in UMAP 2013 Extended Proceedings (2013)

    Google Scholar 

  73. J.W. Pennebaker, M.E. Francis, R.J. Booth, Linguistic Inquiry and Word Count: Liwc 2001 (Lawrence Erlbaum Associates, Mahwah, 2001), p. 71

    Google Scholar 

  74. D. Quercia, M. Kosinski, D. Stillwell, J. Crowcroft, 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 (IEEE, Piscataway, 2011), pp. 180–185 https://doi.org/10.1109/PASSAT/SocialCom.2011.26

    Book  Google Scholar 

  75. L. Quijano-Sanchez, J.A. Recio-Garcia, B. Diaz-Agudo, Personality and social trust in group recommendations, in 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (c) (2010), pp. 121–126. https://doi.org/10.1109/ICTAI.2010.92

  76. D. Rawlings, V. Ciancarelli, Music preference and the five-factor model of the NEO personality inventory. Psychol. Music 25(2), 120–132 (1997). https://doi.org/10.1177/0305735697252003

    Article  Google Scholar 

  77. J.A. Recio-Garcia, G. Jimenez-Diaz, A.A. Sanchez-Ruiz, B. Diaz-Agudo, Personality aware recommendations to groups, in Proceedings of the Third ACM Conference on Recommender Systems - RecSys ’09 (ACM Press, New York, 2009), p. 325. https://doi.org/10.1145/1639714.1639779

    Book  Google Scholar 

  78. P.J. Rentfrow, L.R. Goldberg, R. Zilca, Listening, watching, and reading: the structure and correlates of entertainment preferences. J. Personal. 79(2), 223–58 (2011). https://doi.org/10.1111/j.1467-6494.2010.00662.x

    Article  Google Scholar 

  79. P.J. Rentfrow, S.D. Gosling, The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Personal. Soc. Psychol. 84(6), 1236–1256 (2003). https://doi.org/10.1037/0022-3514.84.6.1236

    Article  Google Scholar 

  80. A. Roshchina, J. Cardiff, P. Rosso, TWIN: personality-based intelligent recommender system. J. Intell. Fuzzy Syst. 28, 2059–2071 (2015). https://doi.org/10.3233/IFS-141484

    Article  Google Scholar 

  81. C. Ross, E.S. Orr, M. Sisic, J.M. Arseneault, M.G. Simmering, R.R. Orr, Personality and motivations associated with facebook use. Comput. Hum. Behav. 25(2), 578–586 (2009)

    Article  Google Scholar 

  82. J. Schrammel, C. Köffel, M. Tscheligi, Personality traits, usage patterns and information disclosure in online communities, in Proceedings of the 23rd British HCI … (2009), pp. 169–174

    Google Scholar 

  83. M. Selfhout, W. Burk, S. Branje, J. Denissen, M. van Aken, W. Meeus, Emerging late adolescent friendship networks and Big Five personality traits: a social network approach. J. Personal. 78(2), 509–538 (2010). https://doi.org/10.1111/j.1467-6494.2010.00625.x

    Article  Google Scholar 

  84. X. Sha, D. Quercia, P. Michiardi, M. Dell’Amico, Spotting trends, in Proceedings of the Sixth ACM Conference on Recommender Systems - RecSys ’12 (ACM Press, New York, 2012), p. 51. https://doi.org/10.1145/2365952.2365967

    Book  Google Scholar 

  85. J. Shen, O. Brdiczka, J. Liu, Understanding email writers: personality prediction from email messages, User Modeling, Adaptation, and Personalization (2013), pp. 318–330. https://doi.org/10.1007/978-3-642-38844-6_29

  86. M. Skowron, M. Tkalčič, B. Ferwerda, M. Schedl, Fusing social media cues, in Proceedings of the 25th International Conference Companion on World Wide Web - WWW ’16 Companion (ACM Press, New York, 2016), pp. 107–108. https://doi.org/10.1145/2872518.2889368. http://dl.acm.org/citation.cfm?doid=2872518.2889368

  87. B.A. Soloman, R.M. Felder, Index of learning styles questionnaire (2014). http://www.engr.ncsu.edu/learningstyles/ilsweb.html

  88. B. Stewart, Personality and play styles: a unified model (2011)

    Google Scholar 

  89. K.W. Thomas, Conflict and conflict management: reflections and update. J. Organ. Behav. 13(3), 265–274 (1992). https://doi.org/10.1002/job.4030130307

    Article  Google Scholar 

  90. N. Tintarev, M. Dennis, J. Masthoff, Adapting recommendation diversity to openness to experience: a study of human behaviour, in User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science, vol. 7899 (I) (2013), pp. 190–202. https://doi.org/10.1007/978-3-642-38844-6_16

  91. A. Tiroshi, T. Kuflik, Domain ranking for cross domain collaborative filtering, in User Modeling, Adaptation, and Personalization (2012), pp. 328–333. https://doi.org/10.1007/978-3-642-31454-4_30

  92. V. Tiwari, A. Ashpilaya, P. Vedita, U. Daripa, P.P. Paltani, Exploring demographics and personality traits in recommendation system to address cold start problem, pp. 361–369 (2020). https://doi.org/10.1007/978-981-15-0936-0_37. http://link.springer.com/10.1007/978-981-15-0936-0_37

  93. M. Tkalčič, Emotions and personality in recommender systems, in Proceedings of the 12th ACM Conference on Recommender Systems - RecSys ’18, vol. 38 (ACM Press, New York, 2018), pp. 535–536. https://doi.org/10.1145/3240323.3241619. http://link.springer.com/10.1007/978-1-4614-7163-9_110161-1 http://dl.acm.org/citation.cfm?doid=3240323.3241619

  94. M. Tkalcic, B.D. Carolis, M.D. Gemmis, A. Odi, A. Košir, Emotions and Personality in Personalized Services. Human–Computer Interaction Series (Springer International Publishing, Cham, 2016). https://doi.org/10.1007/978-3-319-31413-6. http://link.springer.com/10.1007/978-3-319-31413-6

  95. M. Tkalčič, A. Delić, A. Felfernig, Personality, emotions, and group dynamics, in Group Recommender Systems an Introduction, ed. by A. Felfernig, L. Boratto, M. Stettinger, M. Tkalčič (2018), pp. 157–167. https://doi.org/10.1007/978-3-319-75067-5_9. http://link.springer.com/10.1007/978-3-319-75067-5_9

  96. M. Tkalcic, B. Ferwerda, M. Tkalčič, B. Ferwerda, M. Tkalcic, B. Ferwerda, M. Tkalčič, B. Ferwerda, Eudaimonic modeling of Moviegoers, in UMAP ’18: 26th Conference on User Modeling, Adaptation and Personalization (ACM Press, New York, 2018), pp. 163–167. https://doi.org/10.1145/3209219.3209249. http://dl.acm.org/citation.cfm?doid=3209219.3209249

  97. M. Tkalcic, M. Kunaver, A. Košir, J. Tasic, Addressing the new user problem with a personality based user similarity measure, in 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 

  98. M. Tkalčič, A. Košir, J. Tasič, The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata. J. Multimodal User Interfaces 7(1–2), 143–155 (2013). https://doi.org/10.1007/s12193-012-0107-7

    Article  Google Scholar 

  99. M. Tkalčič, M. Kunaver, J. Tasič, A. Košir, Personality based user similarity measure for a collaborative recommender system, in 5th Workshop on Emotion in Human-Computer Interaction-Real World Challenges (2009), p. 30

    Google Scholar 

  100. N. Wang, L. Chen, Y. Yang, The impacts of item features and user characteristics on user’ perceived serendipity of recommendations, in Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’20 (Association for Computing Machinery, New York, 2020), pp. 266–274. https://doi.org/10.1145/3340631.3394863.

    Google Scholar 

  101. P. Winoto, T. Tang, 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 Gener. Comput. 26(3), 209–225 (2008). https://doi.org/10.1007/s00354-008-0041-0

    Article  Google Scholar 

  102. W. Wu, L. Chen, L. He, Using personality to adjust diversity in recommender systems, in Proceedings of the 24th ACM Conference on Hypertext and Social Media - HT ’13 (May) (2013), pp. 225–229. https://doi.org/10.1145/2481492.2481521

  103. W. Wu, L. Chen, Y. Zhao, Personalizing recommendation diversity based on user personality. User Model. User-Adapt. Interact. 28(3), 237–276 (2018). https://doi.org/10.1007/s11257-018-9205-x.

    Article  Google Scholar 

  104. Z. Yusefi, H. Marjan, K. Afsaneh, Improving sparsity and new user problems in collaborative filtering by clustering the personality factors. Electron. Commer. Res. 18(4), 813–836 (2018). https://doi.org/10.1007/s10660-018-9287-x.

    Article  Google Scholar 

  105. C.N. Ziegler, S.M. McNee, J.A. Konstan, G. Lausen, Improving recommendation lists through topic diversification, in Proceedings of the 14th International Conference on World Wide Web, WWW ’05 (ACM, New York, 2005), pp. 22–32. https://doi.org/10.1145/1060745.1060754

    Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia

    Marko Tkalčič

  2. Hong Kong Baptist University, Kowloon, Hong Kong

    Li Chen

Authors
  1. Marko Tkalčič
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marko Tkalčič .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this chapter

Cite this chapter

Tkalčič, M., Chen, L. (2022). Personality and Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2197-4_20

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-0716-2196-7

  • Online ISBN: 978-1-0716-2197-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation