Skip to main content
SpringerLink
Log in

Neural Correlates of Human Decision Making in Recommendation Systems: A Research Proposal

  • Conference paper
  • First Online:
Information Systems and Neuroscience

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 29))

Abstract

Significant research has been conducted on human decision making behavior in recommendation systems during the past decade, yet it remains a challenge to design effective and efficient recommendation systems so that they not only produce useful suggestions and ease the decision making task but also turn it into a pleasurable experience. Algorithms have been designed based on research that highlight individual theoretical constructs yet there is an absence of a comprehensive model of human decision-making. This research offers an insight into the core of this issue by examining the neural correlates of human decision-making using Electroencephalography (EEG). The insights generated maybe used to construct a comprehensive model of human decision making in recommendation systems and generate new design principles for the same.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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.

    The Book Crossing dataset was collected by Cai-Nicolas Ziegler in 2004. It contains 278,858 users’ anonymized demographic data about books.

  2. 2.

    German scientist Korbinian Brodmann named different regions of the brain based on the cytoarchitectural structure of neurons. These areas are referred to as Brodmann Areas.

References

  1. Bonaccio, S., Dalal, R.S.: Advice taking and decision-making: an integrative literature review, and implications for the organizational sciences. Organ. Behav. Hum. Decis. Process. 101(2), 127–151 (2006)

    Article  Google Scholar 

  2. Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31(1), 137–209 (2007)

    Article  Google Scholar 

  3. Xiao, B., Benbasat, I.: Research on the use, characteristics, and impact of e-commerce product recommendation agents: a review and update for 2007–2012. In: Handbook of Strategic e-Business Management, pp. 403–431. Springer Berlin Heidelberg (2014)

    Google Scholar 

  4. Chen, L., de Gemmis, M., Felfernig, A., Lops, P., Ricci, F., Semeraro, G.: Human decision making and recommender systems. ACM Trans. Interact. Intell. Syst. (TiiS) 3(3), 17 (2013)

    Google Scholar 

  5. Biele, G., Rieskamp, J., Krugel, L.K., Heekeren, H.R.: The neural basis of following advice. PLoS Biol. 9(6), e1001089 (2011)

    Article  Google Scholar 

  6. Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile internet. In Proceedings of the third ACM conference on Recommender Systems, pp. 205–208. ACM (2009)

    Google Scholar 

  7. Jameson, A., Willemsen, M.C., Felfernig, A., de Gemmis, M., Lops, P., Semeraro, G., Chen, L.: Human decision making and recommender systems. In Recommender Systems Handbook, pp. 611–648. Springer, Boston, MA (2015)

    Chapter  Google Scholar 

  8. Cerf, M., Garcia-Garcia, M., Kotler, P.: Consumer Neuroscience. MIT Press (2017)

    Google Scholar 

  9. Jannach, D., Zanker, M., Ge, M., Gröning, M.: Recommender systems in computer science and information systems—a landscape of research. In: International Conference on Electronic Commerce and Web Technologies, pp. 76–87. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  10. Dimoka, A., Pavlou, P.A., Davis, F.D.: Research commentary—NeuroIS: the potential of cognitive neuroscience for information systems research. Inf. Syst. Res. 22(4), 687–702 (2011)

    Article  Google Scholar 

  11. Taylor, J.G.: Future directions for neural networks and intelligent systems from the brain imaging research. In: Future Directions for Intelligent Systems and Information Sciences, pp. 191–212. Physica, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Hu, R., Pu, P.: A comparative user study on rating vs. personality quiz based preference elicitation methods. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 367–372. ACM (2009)

    Google Scholar 

  13. Lieberman, M.D.: Social cognitive neuroscience: a review of core processes. Annu. Rev. Psychol. 10(58), 259–289 (2007)

    Article  Google Scholar 

  14. Dimoka, A., Davis, F.D., Gupta, A., Pavlou, P.A., Banker, R.D., Dennis, A.R., Ischebeck, A., Müller-Putz, G., Benbasat, I., Gefen, D., Kenning, P.H.: On the use of neurophysiological tools in IS research: developing a research agenda for NeuroIS. MIS Q. 1, 679–702 (2012)

    Article  Google Scholar 

  15. Brocke, J.V., Riedl, R., Léger, P.M.: Application strategies for neuroscience in information systems design science research. J. Comput. Inf. Syst. 53(3), 1–3 (2013)

    Google Scholar 

  16. Riedl, R., Léger, P.M.: Fundamentals of NeuroIS. In: Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Berlin, Heidelberg (2016)

    Google Scholar 

  17. Pavlou, P., Davis, F., Dimoka, A.: Neuro IS: the potential of cognitive neuroscience for information systems research. In: ICIS 2007 Proceedings, p. 122. (2007)

    Google Scholar 

  18. Bhatt, M., Camerer, C.F.: Self-referential thinking and equilibrium as states of mind in games: fMRI evidence. Games Econ. Behavior. 52(2), 424–459 (2005)

    Article  Google Scholar 

  19. Riedl, R., Mohr, P.N., Kenning, P.H., Davis, F.D., Heekeren, H.R.: Trusting humans and avatars: a brain imaging study based on evolution theory. J. Manage. Inf. Syst. 30(4), 83–114 (2014). Apr 1

    Article  Google Scholar 

  20. Dimoka, A.: What does the brain tell us about trust and distrust? Evidence from a functional neuroimaging study. MIS Q. 1, 373–396 (2010)

    Article  Google Scholar 

  21. Komiak, S.Y., Benbasat, I.: The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Q. 1, 941–960 (2006)

    Article  Google Scholar 

  22. Winston, J.S., Strange, B.A., O’Doherty, J., Dolan, R.J.: Automatic and intentional brain responses during evaluation of trustworthiness of faces. Nat. Neurosci. 5(3), 277 (2002)

    Article  Google Scholar 

  23. Pascual-Marqui, R.D.: Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl D), 5–12 (2002)

    Google Scholar 

  24. Pascual-Marqui, R.D.: Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. arXiv preprint arXiv:0710.3341 (2007)

Download references

Acknowledgements

We are grateful to the BCI Lab at Department of Physics, University of Karachi for helpful suggestions in drafting this paper and providing facilities.

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Institute of Business Administration, Karachi, Pakistan

    Naveen Zehra Quazilbash, Zaheeruddin Asif & Syed Asil Ali Naqvi

Authors
  1. Naveen Zehra Quazilbash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zaheeruddin Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Syed Asil Ali Naqvi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naveen Zehra Quazilbash .

Editor information

Editors and Affiliations

  1. Information Systems and Quantitative Sciences (ISQS), Texas Tech University, Lubbock, TX, USA

    Fred D. Davis

  2. University of Applied Sciences Upper Austria, Steyr, Oberösterreich, Austria

    René Riedl

  3. Department of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

  4. Department of Information Technology, HEC Montréal, Montreal, QC, Canada

    Pierre-Majorique Léger

  5. Department of Information Systems, Kennesaw State University, Kennesaw, GA, USA

    Adriane B. Randolph

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Quazilbash, N.Z., Asif, Z., Naqvi, S.A.A. (2019). Neural Correlates of Human Decision Making in Recommendation Systems: A Research Proposal. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-01087-4_17

Download citation

Publish with us

Policies and ethics

Search

Navigation