Skip to main content
SpringerLink
Log in

Human Decision Making and Recommender Systems

  • Chapter
Recommender Systems Handbook

Abstract

If we assume that an important function of recommender systems is to help people make better choices, it follows that people who design and study recommender systems ought to have a good understanding of how people make choices and how human choice can be supported. This chapter starts with a compact synthesis of research on the various ways in which people make choices in everyday life, in terms of six choice patterns; we explain for each pattern how recommender systems can support its application, both in familiar ways and in ways that have not been explored so far. Similarly, we distinguish six high-level strategies for supporting choice, noting that one strategy is directly supported by recommendation technology but that the others can also be applied fruitfully in recommender systems. We then illustrate how this conceptual framework can be used to shed new light on several fundamental questions that arise in recommender systems research: In what ways can explanations of recommendations support choice processes? What are we referring to when we speak of a person’s “preferences”? What goes on in people’s heads when they rate an item? What is “choice overload”, and how can recommender systems help prevent it? How can recommender systems help choosers to engage in trial and error? What subtle influences on choice can arise when people choose among a small number of options; and how can a recommender system take them into account? One general contribution of the chapter is to generate new ideas about how recommendation technology can be deployed in support of human choice, often in conjunction with other strategies and technologies.

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 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
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.

    There is no crisp distinction in English between “choosing” and “deciding”. We will mostly use the former term, since “decision making” is often associated with complex problems requiring deep thought and analysis, whereas recommender systems are more commonly used in connection with smaller, less complex problems.

  2. 2.

    To avoid clumsy formulations like “him- or herself” when using personal pronouns in a generic way, we will alternate between the masculine and feminine forms on an example-by-example basis.

  3. 3.

    Much more detail on the Aspect and Arcade models will be found in the book-length monograph by Jameson et al. [39].

  4. 4.

    More discussion of the distinction between the contrasting goals of persuasion and choice support is given in [39, Sect. 1.2].

  5. 5.

    Aspect is an acronym formed from the first letters of the six patterns.

  6. 6.

    In the terminology of the Arcade model (Sect. 18.3 below), this strategy is called Evaluate on Behalf of the Chooser. As we will see in that section, recommender systems typically also support choice with applications of other strategies that are not specifically associated with recommendation technology.

  7. 7.

    This type of awareness is often absent, as when the recommender system adapts the order in which a list of options is presented to the user without announcing the fact that it is doing so.

  8. 8.

    This type of advice is often given implicitly, in that the system provides support for one procedure but not for others (e.g., by reminding the chooser of her past experience but providing no information about what other people choose).

  9. 9.

    A thorough discussion of credibility in human and artificial advice giving can be found in Chap. 20.

  10. 10.

    In the recommender systems field, relatively few attempts have been made to measure and model preferences in such a relative way, for example by using interfaces in which users rank a set of items; but see, for instance, the work of Boutilier and colleagues (e.g., [54]).

  11. 11.

    See [2, 77] and [35] for discussions of rating noise.

  12. 12.

    The most widely recounted—and most often overinterpreted—example of choice overload is the “jam study” of Iyengar and Lepper [38].

  13. 13.

    This sort of advice might be given selectively only to choosers who had been identified as likely maximizers with the help of one of the relevant testing scales [64, 80].

  14. 14.

    http://www.di.uniba.it/~swap/DM.

  15. 15.

    http://recex.ist.tugraz.at:8080/RecSysWorkshop2011/.

  16. 16.

    http://recex.ist.tugraz.at/RecSysWorkshop2012.

  17. 17.

    http://recex.ist.tugraz.at/RecSysWorkshop/.

  18. 18.

    http://recex.ist.tugraz.at/intrs2014/.

  19. 19.

    http://dmrsworkshop.inf.unibz.it.

References

  1. Adomavicius, G., Bockstedt, J., Curley, S., Zhang, J.: Recommender systems, consumer preferences, and anchoring effects. In: Proceedings of the Workshop Decisions@RecSys, in Conjunction with the Fourth ACM Conference on Recommender Systems, pp. 35–42. Chicago (2011)

    Google Scholar 

  2. Amatriain, X., Pujol, J., Oliver, N.: I like it …I like it not: Evaluating user ratings noise in recommender systems. In: G.J. Houben, G. McCalla, F. Pianesi, M. Zancanaro (eds.) Proceedings of the Seventeenth International Conference on User Modeling, Adaptation, and Personalization, pp. 247–258. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  3. Betsch, T., Haberstroh, S. (eds.): The Routines of Decision Making. Erlbaum, Mahwah, NJ (2005)

    Google Scholar 

  4. Bettman, J., Luce, M.F., Payne, J.: Constructive consumer choice processes. Journal of Consumer Research 25, 187–217 (1998)

    Article  Google Scholar 

  5. Bhatia, S.: Associations and the accumulation of preference. Psychological Review 120(3), 522–543 (2013)

    Article  Google Scholar 

  6. Bollen, D., Graus, M., Willemsen, M.: Remembering the stars? Effect of time on preference retrieval from memory. In: P. Cunningham, N. Hurley, I. Guy, S.S. Anand (eds.) Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 217–220. ACM, New York (2012)

    Chapter  Google Scholar 

  7. Bollen, D., Knijnenburg, B., Willemsen, M., Graus, M.: Understanding choice overload in recommender systems. In: X. Amatriain, M. Torrens, P. Resnick, M. Zanker (eds.) Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 63–70. ACM, New York (2010)

    Chapter  Google Scholar 

  8. Bonaccio, S., Dalal, R.: Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences. Organizational Behavior and Human Decision Processes 101, 127–151 (2006)

    Article  Google Scholar 

  9. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  10. Burke, R.: Hybrid web recommender systems. In: P. Brusilovsky, A. Kobsa, W. Nejdl (eds.) The Adaptive Web: Methods and Strategies of Web Personalization, pp. 377–408. Springer, Berlin (2007)

    Chapter  Google Scholar 

  11. Camerer, C., Babcock, L., Loewenstein, G., Thaler, R.: Labor supply of New York City cab drivers: One day at a time. In: D. Kahneman, A. Tversky (eds.) Choices, Values, and Frames. Cambridge University Press, Cambridge, UK (2000)

    Google Scholar 

  12. Carson, R., Louviere, J.: A common nomenclature for stated preference elicitation approaches. Environmental and Resource Economics 49(4), 539–559 (2011)

    Article  Google Scholar 

  13. Celma Herrada, O.: Music Recommendation and Discovery in the Long Tail (2008). PhD Thesis, University of Barcelona

    Google Scholar 

  14. Chen, L., de Gemmis, M., Felfernig, A., Lops, P., Ricci, F., Semeraro, G.: Human decision making and recommender systems. ACM Transactions on Interactive Intelligent Systems 3(3) (2013)

    Google Scholar 

  15. Chernev, A.: When more is less and less is more: The role of ideal point availability and assortment in consumer choice. Journal of Consumer Research 30(2), 170–183 (2003)

    Article  Google Scholar 

  16. Cialdini, R.: Influence: The Psychology of Persuasion. HarperCollins, New York (2007)

    Google Scholar 

  17. Cohen, J., McClure, S., Yu, A.: Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society 362, 933–942 (2007)

    Google Scholar 

  18. Conner, M., Armitage, C.: Attitudinal ambivalence. In: W. Crano, R. Prislin (eds.) Attitudes and Attitude Change. Psychology Press, New York (2008)

    Google Scholar 

  19. Cosley, D., Lam, S., Albert, I., Konstan, J., Riedl, J.: Is seeing believing? How recommender systems influence users’ opinions. In: L. Terveen, D. Wixon, E. Comstock, A. Sasse (eds.) Human Factors in Computing Systems: CHI 2003 Conference Proceedings, pp. 585–592. ACM, New York (2003)

    Chapter  Google Scholar 

  20. Edwards, W., Fasolo, B.: Decision technology. Annual Review of Psychology 52, 581–606 (2001)

    Article  Google Scholar 

  21. Faltings, B., Torrens, M., Pu, P.: Solution generation with qualitative models of preferences. Computational Intelligence 20(2), 246–263 (2004)

    Article  MathSciNet  Google Scholar 

  22. Fasolo, B., Hertwig, R., Huber, M., Ludwig, M.: Size, entropy, and density: What is the difference that makes the difference between small and large real-world assortments? Psychology and Marketing 26(3), 254–279 (2009)

    Article  Google Scholar 

  23. Fazio, R.: Attitudes as object-evaluation associations of varying strength. Social Cognition 25(5), 603–637 (2007)

    Article  Google Scholar 

  24. Felfernig, A., Friedrich, G., Gula, B., Hitz, M., Kruggel, T., Melcher, R., Riepan, D., Strauss, S., Teppan, E., Vitouch, O.: Persuasive recommendation: Exploring serial position effects in knowledge-based recommender systems. In: Y. de Kort, W. IJsselsteijn, C. Midden, B. Eggen, B. Fogg (eds.) Proceedings of the Second International Conference on Persuasive Technology, pp. 283–294. Springer, Heidelberg (2007)

    Google Scholar 

  25. Felfernig, A., Gula, B., Leitner, G., Maier, M., Melcher, R., Schippel, S., Teppan, E.: A dominance model for the calculation of decoy products in recommendation environments. In: AISB Symposium on Persuasive Technologies, pp. 43–50 (2008)

    Google Scholar 

  26. Fischhoff, B.: Value elicitation: Is there anything in there? American Psychologist 46(8), 835–847 (1991)

    Article  Google Scholar 

  27. Fishbein, M., Ajzen, I.: Predicting and Changing Behavior: The Reasoned Action Approach. Taylor & Francis, New York (2010)

    Google Scholar 

  28. French, S., Maule, J., Papamichail, N.: Decision Behaviour, Analysis, and Support. Cambridge University Press, Cambridge, UK (2009)

    Book  Google Scholar 

  29. Gawronski, B., Bodenhausen, G.: Unraveling the processes underlying evaluation: Attitudes from the perspective of the APE model. Social Cognition 25(5), 687–717 (2007)

    Article  Google Scholar 

  30. Gigerenzer, G.: Gut Feelings: The Intelligence of the Unconscious. Penguin, London (2007)

    Google Scholar 

  31. Haeubl, G., Murray, K.: Preference construction and persistence in digital marketplace: The role of electronic recommendation agents. Journal of Consumer Psychology 13, 75–91 (2003)

    Article  Google Scholar 

  32. Hastie, R.: Problems for judgment and decision making. Annual Review of Psychology 52, 653–683 (2001)

    Article  Google Scholar 

  33. Hausman, D.: Preference, Value, Choice, and Welfare. Cambridge University Press, Cambridge, UK (2012)

    Google Scholar 

  34. Herlocker, J., Konstan, J., Riedl, J.: Explaining collaborative filtering recommendations. In: P. Dourish, S. Kiesler (eds.) Proceedings of the 2000 Conference on Computer-Supported Cooperative Work. ACM, New York (2000)

    Google Scholar 

  35. Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)

    Article  Google Scholar 

  36. Hsee, C.: Attribute evaluability: Its implications for joint-separate evaluation reversals and beyond. In: D. Kahneman, A. Tversky (eds.) Choices, Values, and Frames. Cambridge University Press, Cambridge, UK (2000)

    Google Scholar 

  37. Huber, J., Payne, W., Puto, C.: Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. Journal of Consumer Research 9, 90–98 (1982)

    Article  Google Scholar 

  38. Iyengar, S., Lepper, M.: When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology 79, 995–1006 (2000)

    Article  Google Scholar 

  39. Jameson, A., Berendt, B., Gabrielli, S., Gena, C., Cena, F., Vernero, F., Reinecke, K.: Choice architecture for human-computer interaction. Foundations and Trends in Human-Computer Interaction 7(1–2), 1–235 (2014)

    Google Scholar 

  40. Jameson, A., Gajos, K.: Systems that adapt to their users. In: J. Jacko (ed.) The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, 3rd edn. CRC Press, Boca Raton, FL (2012)

    Google Scholar 

  41. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge, Cambridge, UK (2011)

    Google Scholar 

  42. Jungermann, H., Fischer, K.: Using expertise and experience for giving and taking advice. In: T. Betsch, S. Haberstroh (eds.) The Routines of Decision Making. Erlbaum, Mahwah, NJ (2005)

    Google Scholar 

  43. Kahneman, D., Ritov, I., Schkade, D.: Economic preferences or attitude expressions? an analysis of dollar responses to public issues. Journal of Risk and Uncertainty 19, 203–235 (1999)

    Article  MATH  Google Scholar 

  44. Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47(2), 263–295 (1979)

    Article  MATH  Google Scholar 

  45. Klein, G.: Sources of Power: How People Make Decisions. MIT Press, Cambridge, MA (1998)

    Google Scholar 

  46. Knijnenburg, B., Reijmer, N., Willemsen, M.: Each to his own: How different users call for different interaction methods in recommender systems. In: B. Mobasher, R. Burke, D. Jannach, G. Adomavicius (eds.) Proceedings of the Fifth ACM Conference on Recommender Systems. ACM, New York (2011)

    Google Scholar 

  47. Levin, I., Gaeth, G.: How consumers are affected by the framing of attribute information before and after consuming the product. Journal of Consumer Research 15, 374–379 (1988)

    Article  Google Scholar 

  48. Levin, I., Schneider, S., Gaeth, G.: All frames are not created equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes 76, 90–98 (1998)

    Article  Google Scholar 

  49. Li, W., Matejka, J., Grossman, T., Konstan, J., Fitzmaurice, G.: Design and evaluation of a command recommendation system for software applications. ACM Transactions on Computer-Human Interaction 18(2) (2011)

    Google Scholar 

  50. Lieberman, D.: Human Learning and Memory. Cambridge University Press, Cambridge, UK (2012)

    Google Scholar 

  51. Lindblom, C.: Still muddling, not yet through. Public Administration Review 39(6), 517–526 (1979)

    Article  Google Scholar 

  52. Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: The automated travel assistant. In: A. Jameson, C. Paris, C. Tasso (eds.) User Modeling: Proceedings of the Sixth International Conference, UM97, pp. 67–78. Springer Wien New York, Vienna (1997)

    Chapter  Google Scholar 

  53. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: F. Ricci, L. Rokach, B. Shapira, P. Kantor (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Berlin (2011)

    Chapter  Google Scholar 

  54. Lu, T., Boutilier, C.: Learning Mallows models with pairwise preferences. In: L. Getoor, T. Scheffer (eds.) Proceedings of the 28th International Conference on Machine Learning, pp. 145–152. ACM, New York (2011)

    Google Scholar 

  55. Mandel, N., Johnson, E.: When web pages influence choice: Effects of visual primes on experts and novices. Journal of Consumer Research 29, 235–245 (2002)

    Article  Google Scholar 

  56. Mandl, M., Felfernig, A.: Improving the performance of unit critiquing. In: J. Masthoff, B. Mobasher, M. Desmarais, R. Nkambou (eds.) Proceedings of the Twentieth International Conference on User Modeling, Adaptation, and Personalization, pp. 176–187. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  57. Mandl, M., Felfernig, A., Teppan, E., Schubert, M.: Consumer decision making in knowledge-based recommendation. Journal of Intelligent Information Systems 37(1), 1–22 (2010)

    Article  Google Scholar 

  58. Mandl, M., Felfernig, A., Tiihonen, J., Isak, K.: Status quo bias in configuration systems. In: Twenty-Fourth International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 105–114. Syracuse, New York (2011)

    Google Scholar 

  59. March, J.: A Primer on Decision Making: How Decisions Happen. The Free Press, New York (1994)

    Google Scholar 

  60. McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Experiments in dynamic critiquing. In: J. Riedl, A. Jameson, D. Billsus, T. Lau (eds.) IUI 2005: International Conference on Intelligent User Interfaces, pp. 175–182. ACM, New York (2005)

    Google Scholar 

  61. McCarthy, K., Salem, Y., Smyth, B.: Experience-based critiquing: Reusing critiquing experiences to improve conversational recommendation. In: I. Bichindaritz, S. Montani (eds.) Case-Based Reasoning Research and Development: Proceedings of ICCBR 2010, pp. 480–494. Springer, Berlin, Heidelberg (2010)

    Chapter  Google Scholar 

  62. McGinty, L., Reilly, J.: On the evolution of critiquing recommenders. In: F. Ricci, L. Rokach, B. Shapira, P. Kantor (eds.) Recommender Systems Handbook, pp. 419–453. Springer, Berlin (2011)

    Chapter  Google Scholar 

  63. Mogilner, C., Rudnick, T., Iyengar, S.: The mere categorization effect: How the presence of categories increases choosers’ perceptions of assortment variety and outcome satisfaction. Journal of Consumer Research 35(2), 202–215 (2008)

    Article  Google Scholar 

  64. Nenkov, G., Morrin, M., Ward, A., Schwartz, B., Hulland, J.: A short form of the Maximization Scale: Factor structure, reliability and validity studies. Judgment and Decision Making 3(5), 371–388 (2008)

    Google Scholar 

  65. Nguyen, T., Kluver, D., Wang, T.Y., Hui, P.M., Ekstrand, M., Willemsen, M., Riedl, J.: Rating support interfaces to improve user experience and recommender accuracy. In: Q. Yang, I. King, Q. Li, P. Pu, G. Karypis (eds.) Proceedings of the Seventh ACM Conference on Recommender Systems, pp. 149–156. ACM, New York (2013)

    Chapter  Google Scholar 

  66. Payne, J., Bettman, J., Johnson, E.: The Adaptive Decision Maker. Cambridge University Press, Cambridge, UK (1993)

    Book  Google Scholar 

  67. Pfeiffer, J.: Interactive Decision Aids in E-Commerce. Springer, Berlin (2012)

    Book  Google Scholar 

  68. Pirolli, P.: Information Foraging Theory: Adaptive Interaction with Information. Oxford University Press, New York (2007)

    Book  Google Scholar 

  69. Plate, C., Basselin, N., Kröner, A., Schneider, M., Baldes, S., Dimitrova, V., Jameson, A.: Recomindation: New functions for augmented memories. In: V. Wade, H. Ashman, B. Smyth (eds.) Adaptive Hypermedia and Adaptive Web-Based Systems: Proceedings of AH 2006, pp. 141–150. Springer, Berlin (2006)

    Chapter  Google Scholar 

  70. Plessner, H., Betsch, C., Betsch, T. (eds.): Intuition in Judgement and Decision Making. Erlbaum, New York (2008)

    Google Scholar 

  71. Pu, P., Chen, L.: Integrating tradeoff support in product search tools for e-commerce sites. In: J. Riedl, M. Kearns, M. Reiter (eds.) Proceedings of the Sixth ACM Conference on Electronic Commerce, pp. 269–278. ACM, New York (2005)

    Chapter  Google Scholar 

  72. Pu, P., Chen, L.: User-involved preference elicitation for product search and recommender systems. AI Magazine 29(4), 93–103 (2008)

    MathSciNet  Google Scholar 

  73. Rachlin, H.: The Science of Self-Control. Harvard, Cambridge, MA (2000)

    Google Scholar 

  74. Rakow, T., Newell, B.: Degrees of uncertainty: An overview and framework for future research on experience-based choice. Journal of Behavioral Decision Making 23, 1–14 (2010)

    Article  Google Scholar 

  75. Read, D., Loewenstein, G., Rabin, M.: Choice bracketing. Journal of Risk and Uncertainty 19, 171–197 (1999)

    Article  MATH  Google Scholar 

  76. Roe, R., Busemeyer, J., Townsend, J.: Multialternative decision field theory: A dynamic connectionist model of decision making. Psychological Review 108(2), 370–392 (2001)

    Article  Google Scholar 

  77. Said, A., Jain, B., Narr, S., Plumbaum, T.: Users and noise: The magic barrier of recommender systems. In: B. Masthoff Judith a., M.C. Desmarais, R. Nkambou (eds.) User Modeling, Adaptation, and Personalization, no. 7379 in Lecture Notes in Computer Science, pp. 237–248. Springer Berlin Heidelberg (2012)

    Google Scholar 

  78. Scheibehenne, B., Greifeneder, R., Todd, P.: Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research 37(3), 409–425 (2010)

    Google Scholar 

  79. Schwartz, B.: The Paradox of Choice: Why More Is Less. HarperCollins, New York (2004)

    Google Scholar 

  80. Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., Lehman, D.: Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology 83(5), 1178–1197 (2002)

    Article  Google Scholar 

  81. Schwarz, N.: Attitude measurement. In: W. Crano, R. Prislin (eds.) Attitudes and Attitude Change, pp. 41–60. Psychology Press, New York (2008)

    Google Scholar 

  82. Simonson, I.: Choice based on reasons: The case of attraction and compromise effects. Journal of Consumer Research 16(2), 158–174 (1989)

    Article  Google Scholar 

  83. Smyth, B.: Case-based recommendation. In: P. Brusilovsky, A. Kobsa, W. Nejdl (eds.) The Adaptive Web: Methods and Strategies of Web Personalization, pp. 342–376. Springer, Berlin (2007)

    Chapter  Google Scholar 

  84. Teppan, E., Felfernig, A.: The asymmetric dominance effect and its role in e-tourism recommender applications. In: Proceedings of the International Conference Wirtschaftsinformatik, pp. 791–800. Vienna (2009)

    Google Scholar 

  85. Teppan, E., Felfernig, A.: Minimization of product utility estimation errors in recommender result set evaluations. Web Intelligence and Agent Systems 10(4), 385–395 (2012)

    Google Scholar 

  86. Teppan, E., Felfernig, A., Isak, K.: Decoy effects in financial service e-sales systems. In: Proceedings of the Workshop Decisions@RecSys, in Conjunction with the Fourth ACM Conference on Recommender Systems, pp. 1–8. Chicago (2011)

    Google Scholar 

  87. Thaler, R., Sunstein, C.: Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press, New Haven (2008)

    Google Scholar 

  88. Tiihonen, J., Felfernig, A.: Towards recommending configurable offerings. International Journal of Mass Customization 3(4), 389–406 (2010)

    Article  Google Scholar 

  89. Toulmin, S.: The Uses of Argument. Cambridge University Press, Cambridge, UK (1958)

    Google Scholar 

  90. Viappiani, P., Boutilier, C.: Regret-based optimal recommendation sets in conversational recommender systems. In: L. Bergman, A. Tuzhilin, R. Burke, A. Felfernig, L. Schmidt-Thieme (eds.) Proceedings of the Third ACM Conference on Recommender Systems, pp. 101–108. ACM, New York (2009)

    Google Scholar 

  91. Victor, P., Cock, M.D., Cornelis, C.: Trust and recommendations. In: F. Ricci, L. Rokach, B. Shapira, P. Kantor (eds.) Recommender Systems Handbook, pp. 645–675. Springer, Berlin (2011)

    Chapter  Google Scholar 

  92. Wakker, P.: Prospect Theory for Risk and Ambiguity. Cambridge University Press, Cambridge, UK (2010)

    Book  MATH  Google Scholar 

  93. Weber, E., Johnson, E.: Constructing preferences from memory. In: S. Lichtenstein, P. Slovic (eds.) The Construction of Preference. Cambridge University Press, Cambridge, UK (2006)

    Google Scholar 

  94. Willemsen, M., Knijnenburg, B., Graus, M., Velter-Bremmers, L., Fu, K.: Using latent features diversification to reduce choice difficulty in recommendation lists. In: Proceedings of the Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces, in Conjunction With the Fifth ACM Conference on Recommender Systems, CEUR Workshop Proceedings, vol. 811, pp. 14–20 (2011)

    Google Scholar 

  95. Wood, W., Neal, D.: A new look at habits and the habit-goal interface. Psychological Review 114(4), 843–863 (2007)

    Article  Google Scholar 

  96. Yates, J.F., Veinott, E., Patalano, A.: Hard decisions, bad decisions: On decision quality and decision aiding. In: S. Schneider, J. Shanteau (eds.) Emerging Perspectives on Judgment and Decision Research. Cambridge University Press, Cambridge, UK (2003)

    Google Scholar 

  97. Zwick, R., Rapoport, A., Lo, A.K., Muthukrishnan, A.: Consumer sequential search: Not enough or too much? Marketing Science 22(4), 503–519 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

The preparation of this chapter benefited from a series of initiatives that have taken place since 2011 under titles similar to the title of this chapter: workshops at the conferences UMAP 2011Footnote 14 and ACM RecSys 2011,Footnote 15 2012,Footnote 16 2013,Footnote 17 and 2014Footnote 18; a special issue of the ACM Transactions on Interactive Intelligent Systems [14]; and a workshop in September of 2014 at the University of Bolzano.Footnote 19

Author information

Authors and Affiliations

  1. DFKI, German Research Center for Artificial Intelligence, Saarbrücken, Germany

    Anthony Jameson

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Martijn C. Willemsen

  3. University of Graz, Graz, Austria

    Alexander Felfernig

  4. Department of Computer Science, University of Bari “Aldo Moro”, Bari, Italy

    Marco de Gemmis, Pasquale Lops & Giovanni Semeraro

  5. Hong Kong Baptist University, Hong Kong, China

    Li Chen

Authors
  1. Anthony Jameson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martijn C. Willemsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Felfernig
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco de Gemmis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pasquale Lops
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Giovanni Semeraro
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anthony Jameson .

Editor information

Editors and Affiliations

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

    Francesco Ricci

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

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Jameson, A. et al. (2015). Human Decision Making and Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7637-6_18

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4899-7636-9

  • Online ISBN: 978-1-4899-7637-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation