Impacts of decoy elements on result set evaluations in knowledge-based recommendation

State-of-the-art recommender systems only partially consider psychological aspects of consumer behaviour. Taking into account those aspects helps to improve the overall understanding of related decision processes. In this paper we investigate influences triggered by so called decoy effects which are known phenomena in decision psychology and marketing. Particularly, we focus on changes in the perceived product utility and the effect on the subjectively felt confidence in a customer's own decision in a potential sales situation. To this end, we report the results of two empirical studies which analyse decoy effects in not yet investigated item/attribute constellations.

[1]  Judith Masthoff,et al.  Is it Me or Is it what I say? Source Image and Persuasion , 2007, PERSUASIVE.

[2]  Allan D. Shocker,et al.  Toward understanding the attraction effect: The implications of product stimulus meaningfulness and familiarity. , 1987 .

[3]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[4]  I. Simonson,et al.  Choice Based on Reasons: The Case of Attraction and Compromise Effects , 1989 .

[5]  Michael J. Sherrard,et al.  The role of sensation seeking and need for cognition on web-site evaluations : a resource-matching perspective , 2005 .

[6]  F. Bolger,et al.  Asymmetric dominance and phantom decoy effects in games , 2007 .

[7]  B. Wernerfelt,et al.  A Rational Reconstruction of the Compromise Effect: Using Market Data to Infer Utilities , 1995 .

[8]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[9]  Alexander Felfernig,et al.  Knowledge-Based Recommender Technologies for Marketing and Sales , 2007, Int. J. Pattern Recognit. Artif. Intell..

[10]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[11]  Jerome R. Busemeyer,et al.  Computational Models of Decision Making , 2003 .

[12]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[13]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[14]  J. F. Quesada,et al.  An Explanation of Decoy Effects without Assuming Numerical Attributes , 2005 .

[15]  Mathias Bauer,et al.  The MAUT-Machine: An Adaptive Recommender System , 2002 .

[16]  Steven Callander,et al.  Context-dependent Voting , 2006 .

[17]  Paramesh Ray.  Independence of Irrelevant Alternatives , 1973 .

[18]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[19]  Kristian J. Hammond,et al.  The FindMe Approach to Assisted Browsing , 1997, IEEE Expert.

[20]  Alexander Felfernig,et al.  Knowledge-based Interactive Selling of Financial Services with FSAdvisor , 2005, AAAI.

[21]  Xue Yang,et al.  Effects of Choice Contrast and Order Sequence on Consumer Judgment and Decision in Comparison- Shopping Assisted Environment , 2004 .

[22]  Alexander Felfernig,et al.  A Dominance Model for the Calculation of Decoy Products in Recommendation Environments , 2008 .

[23]  Gerhard Friedrich.  Elimination of Spurious Explanations , 2004, ECAI.

[24]  Gerhard Friedrich,et al.  An Integrated Environment for the Development of Knowledge-Based Recommender Applications , 2006, Int. J. Electron. Commer..

[25]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[26]  Barry Smyth,et al.  Improving Recommendation Diversity , 2001 .

[27]  D. Kahneman.  Maps of Bounded Rationality: Psychology for Behavioral Economics , 2003 .

[28]  Ian J. Bateman,et al.  Decoy Effects in Choice Experiments and Contingent Valuation: Asymmetric Dominance , 2008, Land Economics.

[29]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.