Skip to main content
SpringerLink
Log in

Inferring Eudaimonia and Hedonia from Digital Traces

  • Chapter
  • First Online:
A Human-Centered Perspective of Intelligent Personalized Environments and Systems

Part of the book series: Human–Computer Interaction Series ((HCIS))

  • 46 Accesses

Abstract

In this chapter we survey methods for inferring two types of characteristics for personalized systems: eudaimonia and hedonia (E and H). The rationale for focusing on these two characteristics is the potential to make good recommendations and the even bigger potential for creating good explanations. We first conceptualize the concepts of E and H for the purposes of personalized systems by disentangling the user preferences from the item characteristics. We proceed on surveying methods for inferring EH user characteristics from digital user traces. We follow with an overview of methods for inferring EH item characteristics from item content. Finally we provide an outlook into the future work.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
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

Notes

  1. 1.

    https://scikit-learn.org/stable/index.html.

  2. 2.

    https://shiny.gold-msi.org/gmsiconfigurator/.

  3. 3.

    https://scikit-learn.org/stable/.

  4. 4.

    https://xite.com/.

References

  1. Koren Y, Rendle S, Bell R (2022) Advances in collaborative filtering. In: Ricci F, Rokach L, Shapira B (eds), Recommender systems handbook. Springer, New York, NY, pp 91–142. https://doi.org/10.1007/978-1-0716-2197-4_3. Accessed 08 Dec 2023

  2. Zhang S, Tay Y, Yao L, Sun A, Zhang C (2022) Deep learning for recommender systems. In: Ricci F, Rokach L, Shapira B (eds), Recommender systems handbook. Springer, New York, NY, pp 173–210. https://doi.org/10.1007/978-1-0716-2197-4_5 Accessed 29 Sep 2023

  3. Tkalcic M, Kosir A, Dobravec S, Tasic J (2011) Emotional properties of latent factors in an image recommender system. Elektrotehniski vestnik 78(4):177–180

    Google Scholar 

  4. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Interact 12(4):331–370 (2002). https://doi.org/10.1023/A:1021240730564. Accessed 14 Nov 2023

  5. Tkalčič M, Chen L (2022) Personality and recommender systems. In: Ricci F, Rokach L, Shapira B (eds), Recommender systems handbook. Springer, New York, NY, pp 757–787. https://link.springer.com/10.1007/978-1-0716-2197-4_20. Accessed 04 Oct 2022

  6. Lex E, Kowald D, Seitlinger P, Tran TNT, Felfernig A, Schedl M (2021) Psychology-informed recommender systems. Foundations and trends ® in information retrieval, vol 15(2). Publisher: Now Publishers, Inc, pp 134–242. https://doi.org/10.1561/1500000090. Accessed 08 Dec 2023

  7. Cantador I, Fernández-Tobías I, Bellogín A, Kosinski M, Stillwell D, Relating personality types with user preferences in multiple entertainment domains, vol 16

    Google Scholar 

  8. Icellioglu S, Ozden MS (2012) Personality effect on decision-making among normal University students and comparing gambling task performances of normal University students with a group of normal adults. Procedia—Soc Behav Sci 46:1010–1014. https://doi.org/10.1016/j.sbspro.2012.05.240. Accessed 04 Oct 2022

  9. Chamorro-Premuzic T, Furnham A (2007) Personality and music: can traits explain how people use music in everyday life? British J Psychol 98(2):175–185. https://doi.org/10.1348/000712606X111177. Accessed 01 Jan 2023

  10. Karumur RP, Konstan JA (2016) Relating newcomer personality to survival and activity in recommender systems. In: Proceedings of the 2016 conference on user modeling adaptation and personalization. ACM, Halifax Nova Scotia Canada, pp 195–205. https://dl.acm.org/doi/10.1145/2930238.2930246. Accessed 04 Oct 2022

  11. Chen L, Wu W, He L (2013) How personality influences users’ needs for recommendation diversity? In: CHI’13 extended abstracts on human factors in computing systems on—CHI EA ’13. ACM Press, Paris, France, p 829. http://dl.acm.org/citation.cfm?doid=2468356.2468505. Accessed 12 Oct. 2022

  12. Mekler ED, Hornbæk K (2016) Momentary pleasure or lasting meaning?: distinguishing eudaimonic and hedonic user experiences. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, San Jose, CA, USA, pp 4509–4520. https://doi.org/10.1145/2858036.2858225. Accessed 04 Oct 2022

  13. Oliver MB, Raney AA (2011) Entertainment as pleasurable and meaningful: identifying hedonic and eudaimonic motivations for entertainment consumption. J Commun 61(5):984–1004. https://doi.org/10.1111/j.1460-2466.2011.01585.x. Accessed 17 Nov 2022

  14. Ryan RM, Deci EL (2001) On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Ann Rev Psychol 52(1):141–166. https://doi.org/10.1146/annurev.psych.52.1.141. Accessed 27 Jan 2023

  15. Botella C, Riva G, Gaggioli A, Wiederhold BK, Alcaniz M, Baños RM (2012) The present and future of positive technologies. Cyberpsychol Behav Soc Netw 15(2):78–84. https://doi.org/10.1089/cyber.2011.0140. Accessed 04 Oct 2022

  16. Bujacz A, Vittersà J, Huta V, Kaczmarek LD (2014) Measuring hedonia and eudaimonia as motives for activities: cross-national investigation through traditional and Bayesian structural equation modeling. Front Psychol 5. https://doi.org/10.3389/fpsyg.2014.00984. Accessed 23 Nov 2022

  17. Gong Y, Xu W (2007) Machine learning for multimedia content analysis, vol 30. Springer, ???

    Google Scholar 

  18. Atrey PK, Hossain MA, El Saddik A, Kankanhalli MS (2010) Multimodal Fus Multimed Anal: A Surv 16(6):345–379. https://doi.org/10.1007/s00530-010-0182-0

    Article  Google Scholar 

  19. Beheshti A, Ghodratnama S, Elahi M, Farhood H (2022) Social data analytics. CRC Press, ???

    Google Scholar 

  20. Hu W, Xie N, Li L, Zeng X, Maybank S (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C: Appl Rev 41(6):797–819. https://doi.org/10.1109/TSMCC.2011.2109710

    Article  Google Scholar 

  21. Brezeale D, Cook DJ (2008) Automatic video classification: a survey of the literature. IEEE Trans Syst Man Cybern Part C: Appl Rev 38(3):416–430. https://doi.org/10.1109/TSMCC.2008.919173

    Article  Google Scholar 

  22. Wang Y, Xing C, Zhou L (2006) Video semantic models: survey and evaluation. Int J Comput Sci Netw Secur (IJCSNS) 6(2):10–20

    MathSciNet  Google Scholar 

  23. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Nat Acad Sci 110(15):5802–5805 (2013). https://doi.org/10.1073/pnas.1218772110. Accessed 30 Dec 2022

  24. Quercia D, Kosinski M, Stillwell D, Crowcroft J (2011) 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, Boston, MA, USA, pp 180–185. https://doi.org/10.1109/PASSAT/SocialCom.2011.26. http://ieeexplore.ieee.org/document/6113111/. Accessed 30 Dec 2022

  25. Golbeck J, Robles C, Turner K (2011) Predicting personality with social media. In: Proceedings of the 2011 annual conference extended abstracts on human factors in computing systems—CHI EA ’11. ACM Press, Vancouver, BC, Canada, p 253. https://doi.org/10.1145/1979742.1979614.http://portal.acm.org/citation.cfm?doid=1979742.1979614. Accessed 04 Oct 2022

  26. Skowron M, Tkalčič M, Ferwerda B, Schedl M (2016) Fusing social media cues: personality prediction from twitter and instagram. In: Proceedings of the 25th international conference companion on world wide web—WWW ’16 companion. ACM Press, Montréal, Québec, Canada, pp 107–108. https://doi.org/10.1145/2872518.2889368http://dl.acm.org/citation.cfm?doid=2872518.2889368. Accessed 04 Oct 2022

  27. Ferwerda B, Tkalčič M (2018) Predicting users’ personality from instagram pictures: using visual and/or content features? In: Proceedings of the 26th conference on user modeling, adaptation and personalization. ACM, Singapore, pp 157–161 (2018). https://doi.org/10.1145/3209219.3209248. https://dl.acm.org/doi/10.1145/3209219.3209248. Accessed 04 Oct 2022

  28. Wang Y, Kosinski M, Deep neural networks are more accurate than humans at detecting sexual orientation from facial images, 12

    Google Scholar 

  29. Tkalčič M, Motamedi E, Barile F, Puc E, Mars Bitenc U (2022) Prediction of hedonic and eudaimonic characteristics from user interactions. In: Adjunct proceedings of the 30th ACM conference on user modeling, adaptation and personalization. ACM, Barcelona, Spain, pp 366–370. https://doi.org/10.1145/3511047.3537656. https://dl.acm.org/doi/10.1145/3511047.3537656. Accessed 04 Oct 2022

  30. Müllensiefen D, Gingras B, Musil J, Stewart L (2014) The musicality of non-musicians: an index for assessing musical sophistication in the general population. PLoS ONE 9(2): 89642. https://doi.org/10.1371/journal.pone.0089642. Accessed 04 Oct 2022

  31. John O, Srivastava S (1999) The big five trait taxonomy: history, measurement, and theoretical perspectives. In: Pervin LA, John OP (eds), Handbook of personality: theory and research, vol 2, 2nd ed. Guilford Press, New York, pp 102–138

    Google Scholar 

  32. Motamedi E, Barile F, Tkalčič M (2022) Prediction of eudaimonic and hedonic orientation of movie watchers. Appl Sci 12(19):9500. https://doi.org/10.3390/app12199500. Accessed 14 Nov 2022

  33. Gosling SD, Rentfrow PJ, Swann WB Jr (2003) A very brief measure of the big-five personality domains. J Res Person 37(6):504–528

    Article  Google Scholar 

  34. Tkalčič M, Ferwerda B (2018) Eudaimonic modeling of moviegoers. In: Proceedings of the 26th conference on user modeling, adaptation and personalization, pp 163–167. ACM, Singapore, Singapore. https://doi.org/10.1145/3209219.3209249. https://dl.acm.org/doi/10.1145/3209219.3209249. Accessed 17 Nov 2022

  35. Motamedi E, Tkalcic M, Szlávik Z (2023) Eudaimonic and hedonic qualities as predictors of music videos’ relevance to users: a human-centric study. In: Adjunct proceedings of the 31st ACM conference on user modeling, adaptation and personalization. ACM, Limassol Cyprus, pp 44–49. https://doi.org/10.1145/3563359.3597415. https://dl.acm.org/doi/10.1145/3563359.3597415 Accessed 12 Sep 2023

  36. Motamedi E, Kholgh DK, Saghari S, Elahi M, Barile F, Tkalcic M (2024) Predicting movies’ eudaimonic and hedonic scores: a machine learning approach using metadata, audio and visual features. Inf Proc Manag

    Google Scholar 

  37. Hrustanovi? S, Kavšek B, Tkalčič M (2021) Recognition of eudaimonic and hedonic qualities from song lyrics. In: Human-Computer interaction Slovenia 2021, 11 Nov 2021, Koper, Slovenia, p 9

    Google Scholar 

  38. Motamedi E, Tkalčič M (2021) Prediction of eudaimonic and hedonic movie characteristics from subtitles. In: Human-Computer interaction Slovenia 2021, 11 Nov 2021, Koper, Slovenia, p 8. https://ceur-ws.org/Vol-3054/paper6.pdf

  39. Puc E (2021) Movie recommender system—psychological constructs and general movie sophistication or movie likeability score and movie genre. PhD thesis, University of Primorska

    Google Scholar 

  40. Duriez B, Meeus J, Vansteenkiste M (2012) Why are some people more susceptible to ingroup threat than others? The importance of a relative extrinsic to intrinsic value orientation. J Res Pers 46(2), 164–172. https://doi.org/10.1016/j.jrp.2012.01.003. Accessed 13 Jan 2024

  41. Grave E, Bojanowski P, Gupta P, Joulin A, Mikolov T (2018) Learning word vectors for 157 languages. In: Proceedings of the international conference on language resources and evaluation (LREC 2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, 6000, Slovenia

    Marko Tkalčič & Elham Motamedi

Authors
  1. Marko Tkalčič
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elham Motamedi
    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. Department of Computer Science and Informatics, Jönköping University, School of Engineering, Jönköping, Sweden

    Bruce Ferwerda

  2. Human-Centric SARL, Paris, France

    Mark Graus

  3. UX Industries & Customer Experience, Product Engineering, SAP SE, Intelligent Enterprise Group, Walldorf, Baden-Württemberg, Germany

    Panagiotis Germanakos

  4. University of Primorska, Koper, Slovenia

    Marko Tkalčič

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tkalčič, M., Motamedi, E. (2024). Inferring Eudaimonia and Hedonia from Digital Traces. In: Ferwerda, B., Graus, M., Germanakos, P., Tkalčič, M. (eds) A Human-Centered Perspective of Intelligent Personalized Environments and Systems. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-031-55109-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-55109-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-55108-6

  • Online ISBN: 978-3-031-55109-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation