Advertisement

Persuasion in Knowledge-Based Recommendation

  • Alexander Felfernig
  • Bartosz Gula
  • Gerhard Leitner
  • Marco Maier
  • Rudolf Melcher
  • Erich Teppan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5033)

Abstract

Recommendation technologies support users in the identification of interesting products and services. Beside the wide-spread approaches of collaborative and content-based filtering, knowledge-based recommender technologies gain an increasing importance due to their capability of deriving recommendations for complex products such as financial services, technical equipment, or consumer goods. The identification of best-fitting products is in many cases a complex decision making task which forces users to fall back to different types of decision heuristics. This phenomenon is explained by the theory of bounded rationality of users which is due to their limited knowledge and computational capacity. Specifically in the context of recommender applications bounded rationality acts as a door opener for different types of persuasive concepts which can influence a user’s attitudes (e.g., in terms of product preferences) and behavior (e.g., in terms of buying behavior). The major goal of this paper is to provide an overview of such persuasive aspects and possible formalizations in knowledge-based recommender systems.

Keywords

Recommender Systems Persuasion Decision Phenomena 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bettman, R., Luce, M., Payne, J.: Constructive Consumer Choice Processes. Journal of Consumer Research 25, 187–217 (1998)CrossRefGoogle Scholar
  2. 2.
    Burke, R.: Knowledge-based Recommender Systems. Encyclopedia of Library and Information Systems 69(32) (2000)Google Scholar
  3. 3.
    Carenini, G., Moore, J.: Generating and evaluating evaluative arguments. Artificial Intelligence 170, 925–952 (2006)CrossRefGoogle Scholar
  4. 4.
    Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: An Environment for the Development of Knowledge-based Recommender Applications. International Journal of Electronic Commerce (IJEC) 11(2), 11–34 (2006)CrossRefGoogle Scholar
  5. 5.
    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: de Kort, Y., IJsselsteijn, W., Midden, C., Eggen, B., Fogg, B.J. (eds.) PERSUASIVE 2007. LNCS, vol. 4744. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Fogg, B.J.: Persuasive Technology – Using Computers to Change What We Think and Do. Morgan Kaufmann Publishers, San Francisco (2003)Google Scholar
  7. 7.
    Friedrich, G.: Elimination of Spurious Explanations. In: European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, pp. 813–817 (2004)Google Scholar
  8. 8.
    Gershberg, F., Shimamura, A.: Serial position effects in implicit and explicit tests of memory. Journal of Experimental Psychology: Learning, Memory, and Cognition 20, 1370–1378 (1994)CrossRefGoogle Scholar
  9. 9.
    Hamilton, R.: Why Do People Suggest What They Do Not Want? Using Context Effects to Influence Others’ Choices. Journal of Consumer Research 29, 492–506 (2003)CrossRefGoogle Scholar
  10. 10.
    Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Kuoa, F.Y., Chub, T.H., Hsuc, M.H., Hsieha, H.S.: An investigation of effort-accuracy trade-off and the impact of self-efficacy on Web searching behaviors. Decision Support Systems 37, 331–342 (2004)CrossRefGoogle Scholar
  13. 13.
    Martin, B., Sherrard, M., Wentzel, D.: The Role of Sensation Seeking and Need for Cognition on Web-Site Evaluations: A Resource-Matching Perspective. Psychology and Marketing 22(2), 109–126 (2005)CrossRefGoogle Scholar
  14. 14.
    McSherry, D.: Explanation in Recommender Systems. Artificial Intelligence Review 24, 179–197 (2005)zbMATHCrossRefGoogle Scholar
  15. 15.
    Nguyen, H., Masthoff, J., Edwards, P.: Modelling a receiver’s position to persuasive arguments. In: International Conference on Persuasive Technology, Stanford, USA (2007)Google Scholar
  16. 16.
    Payne, J.W., Bettman, J.R., Johnson, E.J.: The Adaptive Decision Maker. Cambridge University Press, Cambridge (1993)Google Scholar
  17. 17.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Machine Learning 27, 313–331 (1997)CrossRefGoogle Scholar
  18. 18.
    Pu, P., Chen, L.: Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 542–556 (2007)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Roe, R.M., Busemeyer, J.R., Townsend, T.: Multialternative Decision Field Theory: A Dynamic Connectionist Model of Decision Making. Psychological Review 108(2), 370–392 (2001)CrossRefGoogle Scholar
  20. 20.
    Simon, H.A.: A Behavioral Model of Choice. Quarterly Journal of Economics 69(1), 99–118 (1955)CrossRefGoogle Scholar
  21. 21.
    O’Sullivan, B., Papadopoulos, A., Faltings, B., Pu, P.: Representative Explanations for Over-Constrainted Problems. In: American Conference on Artificial Intelligence (AAAI 2007), Vancouver, Canada, pp. 323–328 (2007)Google Scholar
  22. 22.
    Simonson, I., Tversky, A.: Choice in Context: Tradeoff Contrast and Extremeness Aversion. Journal of Marketing Research 29, 281–295 (1992)CrossRefGoogle Scholar
  23. 23.
    Simonson, I., Nowlis, S., Lemon, K.: The Effect of Local Consideration Sets on Global Choice between Lower Price and Higher Quality. Marketing Science 12, 357–377 (1993)CrossRefGoogle Scholar
  24. 24.
    Smith, S., Levin, I.: Need for Cognition and Choice Framing Effects. Journal of Behavioral Decision Making 9(4), 283–290 (1998)CrossRefGoogle Scholar
  25. 25.
    Wegener, T., Petty, R.: Flexible Correction Processes in Social Judgement: The Role of Naïve Theories in Corrections for Perceived Bias. Journal of Personality and Social Psychology 68, 36–51 (1995)CrossRefGoogle Scholar
  26. 26.
    Winterfeldt, D., Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Bartosz Gula
    • 3
  • Gerhard Leitner
    • 2
  • Marco Maier
    • 3
  • Rudolf Melcher
    • 2
  • Erich Teppan
    • 1
  1. 1.Intelligent Systems and Business Informatics 
  2. 2.Interactive Systems 
  3. 3.Cognitive Psychology UnitKlagenfurt UniversityKlagenfurtAustria

Personalised recommendations