Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Emotions and Personality in Recommender Systems

  • Marko Tkalčič
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_110161-1

Synonyms

Glossary

Personality

A set of personal characteristics that account for individual differences

Affect

The experience of emotion

Emotion

Brief, intense affective state, usually triggered by a stimulus

Mood

Affective state that is non-intense, usually positive or negative

Context

Situation-related factors that influence the decision

Definition

Recommender systems are algorithms that support human decision making by reducing the amount of available options to choose from. Understanding the factors that influence decision making is important for designing better recommender systems. Emotions and personality traits are psychological constructs that are linked to decision making and are used to improve recommendations. Emotions are psychological states that influence decisions. Personality is a human characteristic that accounts for individual differences.

Introduction

Recommender...

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

References

  1. Adomavicius G et al (2011) Context-aware recommender systems. AI Mag 32(3):67–80. Available at: http://www.aaai.org/ojs/index.php/aimagazine/article/view/2364 CrossRefGoogle Scholar
  2. Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59. Available at: http://www.sciencedirect.com/science/article/pii/0005791694900639. Accessed 25 Jul 2011CrossRefGoogle Scholar
  3. Cantador I, Fernández-tobías I, Bellogín A (2013) Relating personality types with user preferences in multiple entertainment domains. EMPIRE 1st workshop on emotions and personality in personalized services, 10 Jun 2013, RomeGoogle Scholar
  4. Carolis B De & Gemmis M De (2015) A multimodal framework for recognizing emotional feedback in conversational recommender systems. RecSys EMPIRE 2015: 3rd workshop on emotions and personality in personalized systems, pp 11–18Google Scholar
  5. Castells P, Hurley NJ, Vargas S (2015) Novelty and diversity in recommender systems. In: Recommender systems handbook. Springer US, Boston, pp 881–918. Available at: http://www.springerlink.com/index/10.1007/978-0-387-85820-3 CrossRefGoogle Scholar
  6. Chen L, Wu W, He L (2016) Personality and recommendation diversity. In: Emotion and personality in personalized services, pp 201–225. doi: 10.1007/978-3-319-31413-6_11 Google Scholar
  7. D’Errico F, Poggi I (2016) Social emotions: a challenge for sentiment analysis and user models. In: Tkalcic M, De Carolis N (eds) Emotions and personality in personalized systems. Springer, Berlin, pp 13–34. doi: 10.1007/978-3-319-31413-6_2 Google Scholar
  8. Ekkekakis P (2012) Affect, mood, and emotion. In: Tenenbaum G, Eklund R, Kamata A (eds) Measurement in sport and exercise psychology. Available at: http://www.humankinetics.com/products/all-products/measurement-in-sport-and-exercise-psychology-wweb-resource-ebook
  9. Ekman P (1999) Basic emotions. In: Dalglesish T, Power MJ (eds) Handbook of cognition and emotion. John Wiley & Sons Ltd, Chichester, pp 45–60. Available at: http://onlinelibrary.wiley.com/doi/10.1002/0470013494.ch3/summary. Accessed 29 Jun 2011Google Scholar
  10. Elahi M et al (2013) Personality-based active learning for collaborative filtering recommender systems. In: Baldoni M et al (eds) AI*IA 2013: advances in artificial intelligence, pp 360–371. Available at: http://link.springer.com/chapter/10.1007/978-3-319-03524-6_31. Accessed 29 Jan 2014CrossRefGoogle Scholar
  11. Farnadi G et al. (2016) Computational personality recognition in social media. User modeling and user-adapted interaction (Special issue on Personality in Personalized Systems). Available at: http://link.springer.com/10.1007/s11257-016-9171-0
  12. Fernández-Tobías I et al (2016) Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model User-Adap Inter. doi: 10.1007/s11257-016-9172-z
  13. Ferwerda B, Schedl M, Tkalcic M (2015a) Personality & emotional states: understanding users’ music listening needs. In: A. Cristea et al. (eds) UMAP 2015a Extended proceedings. Available at: http://ceur-ws.org/Vol-1388/
  14. Ferwerda B, Schedl M, Tkalcic M (2015b) Predicting personality traits with Instagram pictures. In: M. Tkalčič et al. (eds) Proceedings of the 3rd workshop on emotions and personality in personalized systems 2015 – EMPIRE ’15, New York, ACM Press, pp 7–10. Available at: http://dl.acm.org/citation.cfm?doid=2809643.2809644
  15. Finnerty AN, Lepri B, Pianesi F (2016) Acquisition of personality. In: Tkalčič M, De Carolis B, de Gemmis M, Odić A, Košir A (eds) Emotions and personality in personalized services: models, evaluation and applications. Springer International Publishing, Cham, pp 81–99. ISBN:978-3-319-31413-6.CrossRefGoogle Scholar
  16. Gemmis MD et al (2015) An investigation on the serendipity problem in recommender systems. Inf Process Manag 51(5):695–717. doi: 10.1016/j.ipm.2015.06.008 CrossRefGoogle Scholar
  17. Golbeck J et al. (2011) Predicting personality from Twitter. In: 2011 I.E. Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 I.E. Third Int’l Conference on Social Computing. IEEE, pp. 149–156. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6113107. Accessed 23 Sep 2014
  18. Goldberg LR (1998) What is beyond the big five? J Pers 66(4):495–524. doi: 10.1111/1467-6494.00022 MathSciNetCrossRefGoogle Scholar
  19. Hu R, Pu P (2010) Using personality information in collaborative filtering for new users. In: Proceedings of the 2nd ACM RecSys’10 workshop on recommender systems and the social web, pp 17–24. Available at: http://www.dcs.warwick.ac.uk/~ssanand/RSWeb_files/Proceedings_RSWEB-10.pdf#page=23. Accessed 3 May 2011
  20. Jameson A et al (2015) Human decision making and recommender systems. In: Recommender systems handbook. Springer US, Boston, pp 611–648. Available at: http://www.springerlink.com/index/10.1007/978-0-387-85820-3 CrossRefGoogle Scholar
  21. John OP, Srivastava S (1999) The big five trait taxonomy: history, measurement, and theoretical perspectives. In: Handbook of personality: theory and research, vol 2(510), pp 102–138. http://books.google.com/books?hl=en&lr=&id=b0yalwi1HDMC&oi=fnd&pg=PA102&dq=The+big-five+trait+taxonomy:+History,+Measurement,+and+Theoretical+Perspectives.&ots=756zS6ZtPk&sig=-3pfI7eNKlyZLlJYEmwdDYeJ82Y\n. http://scholar.google.de/scholar?hl=de&q=john+sriva
  22. Joho H, Staiano J, Sebe N (2011) Looking at the viewer: analysing facial activity to detect personal highlights of multimedia contents. Multimed Tools Appl. Available at: http://www.springerlink.com/index/Q2475134375M08N3.pdf. Accessed 5 Apr 2012
  23. Kahneman D (2003) A perspective on judgment and choice: mapping bounded rationality. Am Psychol 58(9):697–720. Available at: http://www.ncbi.nlm.nih.gov/pubmed/14584987. Accessed 15 Jul 2010CrossRefGoogle Scholar
  24. Kahneman D (2013) Thinking, fast and slow. New York: Farrar, Straus and Giroux. Available at: http://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374275637
  25. Kaminskas M, Ricci F (2016) Emotion-based matching of music to places. pp 287–310. Available at: http://link.springer.com/10.1007/978-3-319-31413-6_14
  26. Karumur RP, Nguyen TT, Konstan JA. (2016) Exploring the value of personality in predicting rating behaviors. Proceedings of the 10th ACM conference on recommender systems – RecSys’16, pp.139–142. Available at: http://dl.acm.org/citation.cfm?doid=2959100.2959140
  27. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci USA 110(15):5802–5805. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3625324&tool=pmcentrez&rendertype=abstract. Accessed 11 Mar 2013CrossRefGoogle Scholar
  28. Košir A et al (2011) Database for contextual personalization. Elektrotehniški vestnik 78(5):270–274. Available at: http://ev.fe.uni-lj.si/5-2011/Kosir.pdf. Accessed 30 Jan 2014Google Scholar
  29. Kraaykamp G, van Eijck K (2005) Personality, media preferences, and cultural participation. Personal Individ Differ 38(7):1675–1688CrossRefGoogle Scholar
  30. Lonsdale AJ, North AC (2011) Why do we listen to music? a uses and gratifications analysis. Br J Psychol 102(1):108–134. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21241288. Accessed 20 Aug 2014CrossRefGoogle Scholar
  31. Matz S, Chan YWF, Kosinski M (2016) Models of personality. In: Tkalčič M et al (eds) Emotions and Personality in Personalized Services: Models, Evaluation and Applications. Springer International Publishing, pp. 35–54. ISBN 978-3-319-31413-6 doi: 10.1007/978-3-319-31413-6_3
  32. McCrae RR, Costa PT (1987) Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol 52(1):81–90. Available at: http://www.ncbi.nlm.nih.gov/pubmed/3820081 CrossRefGoogle Scholar
  33. McCrae RR, John OP (1992) An introduction to the five-factor model and its applications. J Pers 60(2):175–215CrossRefGoogle Scholar
  34. Mehrabian A (1996) Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr Psychol 14(4):261–292. Available at: http://www.springerlink.com/index/10.1007/BF02686918 MathSciNetCrossRefGoogle Scholar
  35. Neidhardt J et al (2015) A picture-based approach to recommender systems. Inf Tech Tour 15(1):49–69. Available at: http://link.springer.com/10.1007/s40558-014-0017-5 CrossRefGoogle Scholar
  36. Odić A et al (2013) Predicting and detecting the relevant contextual information in a movie-recommender system. Interact Comput 25(1):74–90. Available at: http://iwc.oxfordjournals.org/content/25/1/74.short. Accessed 17 Apr 2013CrossRefGoogle Scholar
  37. Odić A, Košir A, Tkalčič M (2016) Affective and personality corpora. In: Tkalcic M et al (eds) Emotions and personality in personalized services. Springer, pp 163–178. Available at: http://link.springer.com/10.1007/978-3-319-31413-6_9
  38. Oliver MB (2008) Tender affective states as predictors of entertainment preference. J Commun 58(1):40–61CrossRefGoogle Scholar
  39. Pariser E (2011) The filter bubble: what the internet is hiding from you. Penguin Press, New York. ISBN: 978-1-59420-300-8Google Scholar
  40. Picard RW (1995) Affective computing. The MIT Press. Available at: http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0262161702. Accessed 10 Sep 2012
  41. Quercia D et al. (2011) Our twitter profiles, our selves: predicting personality with twitter. In: Proceedings – 2011 I.E. international conference on privacy, security, risk and trust and IEEE international conference on social computing, PASSAT/SocialCom 2011. IEEE, pp. 180–185. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6113111. Accessed 23 Feb 2013
  42. Rentfrow P, Goldberg L, Zilca R (2011) Listening, watching, and reading: the structure and correlates of entertainment preferences. J Pers 79(2):223–258. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2964424&tool=pmcentrez&rendertype=abstract. Accessed 13 Aug 2013CrossRefGoogle Scholar
  43. Rentfrow PJ, Gosling SD (2003) The do re mi’s of everyday life: the structure and personality correlates of music preferences. J Pers Soc Psychol 84(6):1236–1256. Available at: http://doi.apa.org/getdoi.cfm?doi=10.1037/0022-3514.84.6.1236. Accessed 6 Mar 2013CrossRefGoogle Scholar
  44. Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer US, Boston, pp 1–34. Available at: http://www.springerlink.com/index/10.1007/978-0-387-85820-3 CrossRefGoogle Scholar
  45. Saunders GB, Stanton JL (1976) Personality as influencing factor in decision making. Organ Behav Hum Perform 15(2):241–257. Available at: http://linkinghub.elsevier.com/retrieve/pii/0030507376900398. Accessed 11 Sep 2014CrossRefGoogle Scholar
  46. Schuller BW (2016) Acquisition of affect. pp 57–80. Available at: http://link.springer.com/10.1007/978-3-319-31413-6_4
  47. Soleymani M et al (2015) Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans Affect Comput 3045(c):1–1. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7112127 Google Scholar
  48. Soleymani M et al (2014) Corpus development for affective video indexing. IEEE Trans Multimed 16(4):1075–1089CrossRefGoogle Scholar
  49. Stewart B (2011) Personality and play styles: a unified model. Gamasutra. http://www. gamasutra. com/view/feature/…, pp 1–11. Available at: http://www.gamasutra.com/view/feature/134842/personality_and_play_styles_a_.php
  50. Thomas KW (1992) Conflict and conflict management: reflections and update. J Organ Behav 13(3):265–274. Available at: http://onlinelibrary.wiley.com/doi/10.1002/job.4030130307/full. Accessed 24 Sep 2014CrossRefGoogle Scholar
  51. Tkalčič M et al. (2011) Affective recommender systems: the role of emotions in recommender systems. In: A Felfernig et al. (eds.) Joint proceedings of the RecSys 2011 Workshop on human decision making in recommender systems (Decisions@RecSys’11) and user-centric evaluation of recommender systems and their interfaces-2 (UCERSTI 2) affiliated with the 5th ACM Conference on Recommender, pp 9–13. Available at: http://ceur-ws.org/Vol-811/paper2.pdf
  52. Tkalčič M et al. (2009) Personality based user similarity measure for a collaborative recommender system. In: C Peter et al. (eds) 5th Workshop on emotion in human-computer interaction-real world challenges, p 30. Available at: http://publica.fraunhofer.de/documents/N-113443.html. Accessed 23 Sep 2010
  53. Tkalčič M, Burnik U, Košir A (2010) Using affective parameters in a content-based recommender system for images. User Model User-Adapt Interact 20(4):279–311. Available at: http://www.springerlink.com/content/3l2p657572rt4j11. Accessed 2 Sep 2011CrossRefGoogle Scholar
  54. Tkalčič M, Košir A, Tasič J (2013a) The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata. J Multimodal User In 7(1–2):143–155. Available at: http://www.springerlink.com/index/10.1007/s12193-012-0107-7. Accessed 15 Mar 2013Google Scholar
  55. Tkalčič M, Odić A, Košir A (2013b) The impact of weak ground truth and facial expressiveness on affect detection accuracy from time-continuous videos of facial expressions. Inf Sci 249:13–23. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0020025513004295. Accessed 4 Oct 2013CrossRefGoogle Scholar
  56. Vinciarelli A (2016) Computing technologies for social signals. In: Tkalčič M, De Carolis B et al (eds) Emotions and personality in personalized services, Human-computer interaction series. Springer, pp 101–118. Available at: http://link.springer.com/10.1007/978-3-319-31413-6_6
  57. Vinciarelli A, Mohammadi G (2014) A survey of personality computing. IEEE Trans Affect Comput 3045(c):1–1. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6834774. Accessed 5 Jul 2014Google Scholar
  58. Vodlan T, Tkalčič M, Košir A (2015) The impact of hesitation, a social signal, on a user’s quality of experience in multimedia content retrieval. Multimed Tools Appl 74(17):6871–6896. Available at: http://link.springer.com/10.1007/s11042-014-1933-2. Accessed 31 Mar 2014CrossRefGoogle Scholar
  59. Zheng Y, Mobasher B, Burke R (2016) Emotions in context-aware recommender systems. pp 311–326. Available at: http://link.springer.com/10.1007/978-3-319-31413-6_15

Recommended Reading

  1. Tkalčič M et al. (eds) (2016) Emotions and personality in personalized services, Springer International Publishing. Available at: http://www.springer.com/gp/book/9783319314112. Accessed 25 May 2016

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBozen-BolzanoItaly

Section editors and affiliations

  • Giovanni Semeraro
    • 1
  • Cataldo Musto
    • 2
  1. 1.Department of Computer ScienceUniversity of Bari "Aldo Moro"BariItaly
  2. 2.BariItaly