Do recommender systems manipulate consumer preferences? A study of anchoring effects

Information Systems Research

Volumn 24, Issue 4, 2013, Pages 956-975

  Adomavicius, Gediminas ^a   Bockstedt, Jesse C ^b   Curley, Shawn P ^a   Zhang, Jingjing ^c  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c INDIANA UNIVERSITY   (United States)

Author keywords

Anchoring effects; Behavioral decision making; Behavioral economics; Electronic commerce; Experimental research; Preferences; Recommender systems

Indexed keywords

BEHAVIORAL RESEARCH; COMMERCE; DECISION MAKING; ECONOMICS; ELECTRONIC COMMERCE;

ANCHORING EFFECTS; BEHAVIORAL DECISION MAKINGS; BEHAVIORAL ECONOMICS; EXPERIMENTAL RESEARCH; PREFERENCES;

RECOMMENDER SYSTEMS;

EID: 84891819871     PISSN: 10477047     EISSN: 15265536     Source Type: Journal    
DOI: 10.1287/isre.2013.0497     Document Type: Article

References (43)

1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommendation system: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge and Data Engrg. 17(6):734-749.
2. Ariely D, Lewenstein G, Prelec D (2003) "Coherent arbitrariness": Stable demand curves without stable preferences. Quart. J. Econom. 118:73-105.
3. Bell RM, Koren Y (2007) Improved Neighborhood-based Collaborative Filtering. Berkhin P, Caruana R, Wu X, eds. KDD Cup and Workshop (ACM, New York), 7-14.
4. Bennet J, Lanning S (2007) The Netflix Prize. Berkhin P, Caruana R, Wu X, eds. KDD Cup and Workshop (ACM, New York), 3-6.
5. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Cooper G, Moral S, eds. Fourteenth Conf. Uncertainty in Artificial Intelligence, Madison, WI (Morgan Kaufmann, San Francisco), 43-52.
6. Chapman G, Bornstein B (1996) The more you ask for, the more you get: Anchoring in personal injury verdicts. Appl. Cognitive Psych. 10:519-540.
7. Chapman G, Johnson E (2002) Incorporating the irrelevant: Anchors in judgments of belief and value. Gilovich T, Griffin D, Kahneman D, eds. Heuristics and Biases: The Psychology of Intuitive Judgment (Cambridge University Press, Cambridge, UK), 120-138.
8. Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (Lawrence Erlbaum Associates, Hillsdale, NJ), 273-288.
9. Cosley D, Lam S, Albert I, Konstan JA, Riedl J (2003) Is seeing believing? How recommender interfaces affect users' opinions.
10. Cockton G, Korhonen P, eds., CHI 2003 Conference, Fort Lauderdale FL (ACM, New York), 585-592.
11. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans. Inform. Systems 22(1):143-177.
12. Epley N, Gilovich T (2010) Anchoring unbound. J. Consumer Psych. 20:20-24.
13. Flynn LJ (2006) Like this? You'll hate that. (Not all Web recommendations are welcome.) New York Times, (January 23) http://www.nytimes.com/2006/01/23/technology/23recommend.html.
14. Funk S (2006) Netflix Update: Try This at Home, (December 11, 2006). Retrieved January, 2010), http://sifter.org/~simon/journal/20061211.html.
15. Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: A constant time collaborative filtering algorithm. Inform. Retrieval 4(July):133-151.
16. Jacowitz KE, Kahneman D (1995) Measures of anchoring in estimation tasks. Personality Soc. Psych. Bull. 21:1161-1166.
17. Johnson JEV, Schnytzer A, Liu S (2009) To what extent do investors in a financial market anchor their judgments excessively? Evidence from the Hong Kong horserace betting market. J. Behav. Decision Making 22:410-434.
18. Komiak S, Benbasat I (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quart. 30(4):941-960.
19. Koren Y (2009) The BellKor solution to the Netflix grand prize. NetflixPrize.com. http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf.
20. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput. Soc. 42:30-37.
21. Ku G, Galinsky AD, Murnighan JK (2006) Starting low but ending high: A reversal of the anchoring effect in auctions. J. Personality Soc. Psych. 90:975-986.
22. Lam S, Riedl J (2004) Shilling recommender systems for fun and profit. Feldman S, Uretsky M, eds. 13th Internat. Conf. World Wide Web, New York City, NY (ACM, New York), 353-402.
23. Lichtenstein S, Slovic P, eds. (2006) The Construction of Preference (Cambridge University Press, Cambridge, UK).
24. McNee SM, Lam SK, Konstan JA, Riedl J (2003) Interfaces for eliciting new user preferences in recommender systems. User Modeling 2003, Proc., (Springer-Verlag, Berlin), 178-187.
25. Mobasher B, Burke R, Bhaumik R, Williams C (2007) Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Tech. 7(4):Article 23.
26. Mussweiler T, Strack F (2000) Numeric judgments under uncertainty: The role of knowledge in anchoring. J. Experiment. Soc. Psych. 36:495-518.
27. Northcraft G, Neale M (1987) Experts, amateurs, and real estate: An anchoring-and-adjustment perspective on property pricing decisions. Organ. Behav. Human Decision Processes 39:84-97.
28. Piotte M, Chabbert M (2009) The pragmatic theory solution to the Netflix grand prize, (August, 2009), http://www.netflixprize .com/assets/GrandPrize2009_BPC_PragmaticTheory.pdf.
29. Pu P, Chen L (2007) Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20(August 6): 542-556.
30. Russo JE (2010) Understanding the effect of a numerical anchor. J. Consumer Psych. 20:25-27.
31. Sarwar B, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Shen V, Saito N, Lyu M, Zurko M, eds., 10th Intl. WWW Conf., Hong Kong (ACM, New York), 285-295.
32. Schkade DA, Johnson EJ (1989) Cognitive processes in preference reversals. Organ. Behav. Human Decision Processes 44:203-231.
33. Schonfeld E (2007) Click here for the upsell. CNNMoney.com (July). http://money.cnn.com/magazines/business2/business2_archive /2007/07/01/100117056/index.htm.
34. Shih W, Kaufman S, Spinola D (2007) Netflix. Harvard Bus. School, Case 9-607-138, May 31.
35. Strack F, Mussweiler T (1997) Explaining the enigmatic anchoring effect: Mechanisms of selective accessibility. J. Personality Soc. Psych. 73:437-446.
36. Swearingen K, Sinha R (2001) Beyond algorithms: An HCI perspective on recommender systems. Herlocker J, ed., ACM SIGIR 2001 Workshop on Recommender Systems, New Orleans, LA.
37. Thorsteinson T, Breier J, Atwell A, Hamilton C, Privette M (2008) Anchoring effects on performance judgments. Organ. Behav. Human Decision Processes 107:29-40.
38. Tintarev N, Masthoff J (2011) Designing and evaluating explanations for recommender systems. Ricci F, Rokach L, Shapira B, Kantor P, eds. Recommender Systems Handbook (Springer, New York), 479-510.
39. Töscher A, Jahrer M, Bell R (2009) The bigchaos solution to the Netflix grand prize, (September 5, 2009), http://www.netflixprize .com/assets/GrandPrize2009_BPC_BigChaos.pdf.
40. Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185:1124-1131.
41. Wang W, Benbasat I (2007) Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. J. Management Inform. Systems 23(4):217-246.
42. Wilson R, Pettijohn J (2010) Tracking online ad campaigns: A primer. J. Direct, Data and Digital Marketing Practice 12:69-82.
43. Yoo K, Gretzel U (2011) Creating more credible and persuasive recommender systems: The influence of source characteristics on recommender system evaluations. Ricci F, Rokach L, Shapira B, Kantor P, eds. Recommender Systems Handbook (Springer, New York), 455-477.