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Personality, Emotions, and Group Dynamics

  • Alexander Felfernig
  • Ludovico Boratto
  • Martin Stettinger
  • Marko Tkalčič
Chapter
First Online:
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

The methods and techniques introduced in the previous chapters provide a basic means to aggregate the preferences of individual group members and to determine recommendations suitable for the whole group. However, preference aggregation can go beyond the integration of the preferences of individual group members. In this chapter, we show how to take into account the aspects of personality, emotions, and group dynamics when determining item predictions for groups. We summarize research related to the integration of these aspects into recommender systems, and provide some selected examples.

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References

  1. 1.
    M. Asgari, G. Kiss, J. van Santen, I. Shafran, X. Song, Automatic measurement of affective valence and arousal in speech, in IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) (IEEE, New York, 2014), pp. 965–969Google Scholar
  2. 2.
    S. Barsade, The ripple effect: emotional contagion and its influence on group behavior. Adm. Sci. Q. 47(4), 644–675 (2002)CrossRefGoogle Scholar
  3. 3.
    S. Berkovsky, J. Freyne, Group-based recipe recommendations: analysis of data aggregation strategies, in 4th ACM Conference on Recommender Systems, Barcelona, 2010, pp. 111–118Google Scholar
  4. 4.
    R. Brown, Group Processes (Blackwell Publishing, Malden, 2012)Google Scholar
  5. 5.
    I. Christensen, S. Schiaffino, Social influence in group recommender systems. Online Inf. Rev. 38(4), 524–542 (2014)CrossRefGoogle Scholar
  6. 6.
    A. Delic, J. Neidhardt, T. Nguyen, R. Ricci, Research methods for group recommender systems, in RecTour 2016, Boston, MA, 2016Google Scholar
  7. 7.
    F. D’Errico, I. Poggi, Social Emotions. A Challenge for Sentiment Analysis and User Models. HumanComputer Interaction Series (Springer, Berlin, 2016), pp. 13–34Google Scholar
  8. 8.
    A. Felfernig, M. Jeran, G. Ninaus, F. Reinfrank, S. Reiterer, M. Stettinger, Basic approaches in recommendation systems, in Recommendation Systems in Software Engineering (Springer, New York, 2013), pp. 15–37Google Scholar
  9. 9.
    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-Adap. Inter. 26(2–3), 221–255 (2016)CrossRefGoogle Scholar
  10. 10.
    A. Finnerty, B. Lepri, F. Pianesi, Acquisition of personality, in Emotions and Personality in Personalized Services: Models, Evaluation and Applications, vol. 1181, ed. by M. Tkalčič, B. De Carolis, M. de Gemmis, A. Odić, A. Košir (Springer, Berlin, 2016), pp. 81–99Google Scholar
  11. 11.
    D. Forsyth, Group Dynamics (Thomson Higher Education, Belmont, 2006)Google Scholar
  12. 12.
    M. Gartrell, X. Xing, Q. Lv, A. Beach, R. Han, S. Mishra, K. Seada, Enhancing group recommendation by incorporating social relationship interactions, in International Conference on Supporting Group Work (ACM GROUP), Sanibel, FL, 2010, pp. 97–106Google Scholar
  13. 13.
    J. Golbeck, C. Robles, K. Turner, Predicting personality with social media, in Annual Conference on Human Factors in Computing Systems - CHI EA ’11 (2011), pp. 253–262Google Scholar
  14. 14.
    J. Gorla, N. Lathia, S. Robertson, J. Wang, Probabilistic group recommendation via information matching, in 22nd WWW Conference, Rio de Janeiro, 2013, pp. 495–504Google Scholar
  15. 15.
    E. Hatfield, J. Cacioppo, R. Rapson, Emotional contagion. Curr. Dir. Psychol. Sci. 2(3), 96–99 (1993)CrossRefGoogle Scholar
  16. 16.
    A. Ho, I. Menezes, Y. Tagmouti, E-MRS: emotion-based movie recommender system, in IADIS e-Commerce Conference (2006), pp. 1–8Google Scholar
  17. 17.
    O. John, S. Srivastava, The big five trait taxonomy: history, measurement, and theoretical perspectives, in Handbook of Personality: Theory and Research, ed. by L. Pervin, O. John, vol. 2 (Guilford Press, New York, 1999), pp. 102–138Google Scholar
  18. 18.
    R. Kilmann, K. Thomas, Developing a forced-choice measure of conflict-handling behavior: the ‘MODE’ instrument. Educ. Psychol. Meas. 37(2), 309–325 (1977)CrossRefGoogle Scholar
  19. 19.
    M. Kosinski, D. Stillwell, T. Graepel, Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. U. S. A. 110(15), 5802–5805 (2013)CrossRefGoogle Scholar
  20. 20.
    A. Kramer, J. Guillory, J. Hancock, Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. U. S. A. 111(24), 8788–8790 (2014)CrossRefGoogle Scholar
  21. 21.
    J. Masthoff, Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User-Adap. Inter. 14(1), 37–85 (2004)CrossRefGoogle Scholar
  22. 22.
    J. Masthoff, A. Gatt, In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User-Adap. Inter. 16(3–4), 281–319 (2006)CrossRefGoogle Scholar
  23. 23.
    S. Matz, Y. Chan, M. Kosinski, Models of personality, in Emotions and Personality in Personalized Services: Models, Evaluation and Applications, ed. by M. Tkalčič, B. De Carolis, M. de Gemmis, A. Odić, A. Košir (Springer, Berlin, 2016), pp. 35–54Google Scholar
  24. 24.
    R. McCrae, O. John, An introduction to the five-factor model and its applications. J. Pers. 60(2), 175–215 (1992)CrossRefGoogle Scholar
  25. 25.
    A. Mehrabian, Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr. Psychol. 14(4), 261–292 (1996)MathSciNetCrossRefGoogle Scholar
  26. 26.
    J. Neidhardt, L. Seyfang, R. Schuster, H. Werthner, A picture-based approach to recommender systems. Inf. Technol. Tour. 15(1), 49–69 (2015)CrossRefGoogle Scholar
  27. 27.
    T. Nguyen, F. Ricci, Combining long-term and discussion-generated preferences in group recommendations, in 25th ACM Conference on User Modeling Adaptation and Personalization, Bratislava (ACM, New York, 2017), pp. 377–378Google Scholar
  28. 28.
    D. Quercia, M. Kosinski, D. Stillwell, J. Crowcroft, Our twitter profiles, our selves: predicting personality with twitter, in Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011 (IEEE, New York, 2011), pp. 180–185Google Scholar
  29. 29.
    L. Quijano-Sanchez, J. Recio-García, B. Díaz-Agudo, G. Jiménez-Díaz, Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. 4(1), 8:1–8:30 (2006)Google Scholar
  30. 30.
    L. Quijano-Sanchez, J. Recio-García, B. Díaz-Agudo, Personality and social trust in group recommendations, in 22nd International Conference on Tools with Artificial Intelligence, Arras, 2010, pp. 121–126Google Scholar
  31. 31.
    L. Quijano-Sánchez, D. Bridge, B. Díaz-Agudo, J. Recio-García, A case-based solution to the cold-start problem in group recommenders, in 23rd International Conference on Artificial Intelligence (IJCAI 2013) (2013), pp. 3042–3046Google Scholar
  32. 32.
    E. Quintarelli, E. Rabosio, L. Tanca, Recommending new items to ephemeral groups using contextual user influence, in RecSys (2016), pp. 285–292Google Scholar
  33. 33.
    J. Recio-Garcia, G. Jimenez-Diaz, A. Sanchez-Ruiz, B. Diaz-Agudo, Personality aware recommendations to groups, in ACM Conference on Recommender Systems (RecSys’09), New York, 2009, pp. 325–328Google Scholar
  34. 34.
    B. Saha, L. Getoor, Group proximity measure for recommending groups in online social networks, in SNA-KDD Workshop 2008, Las Vegas, 2008, pp. 1–9Google Scholar
  35. 35.
    B. Schuller, Acquisition of affect, in Emotions and Personality in Personalized Services: Models, Evaluation and Applications, ed. by M. Tkalčič, B. De Carolis, M. de Gemmis, A. Odić, A. Košir (Springer International Publishing, Cham, 2016), pp. 57–80Google Scholar
  36. 36.
    M. Skowron, B. Ferwerda, M. Tkalčič, M. Schedl, Fusing social media cues: personality prediction from twitter and instagram, in 25th International Conference Companion on World Wide Web (2016), pp. 107–108Google Scholar
  37. 37.
    M. Tkalčič, L. Chen, Personality and recommender systems, in Recommender Systems Handbook (Springer, New York, 2015), pp. 715–739Google Scholar
  38. 38.
    M. Tkalčič, U. Burnik, A. Košir, Using affective parameters in a content-based recommender system for images. User Model. User-Adap. Inter. 20(4), 279–311 (2010)CrossRefGoogle Scholar
  39. 39.
    Y. Zheng, R. Burke, B. Mobasher, The role of emotions in context-aware recommendation, in ACM Conference on Recommender Systems (RecSys’13), Hong Kong, 2013, pp. 21–28Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Ludovico Boratto
    • 2
  • Martin Stettinger
    • 1
  • Marko Tkalčič
    • 3
  1. 1.Institute for Software TechnologyGraz University of TechnologyGrazAustria
  2. 2.EURECATCentre Tecnológico de CatalunyaBarcelonaSpain
  3. 3.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly

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