Investigating the Decision-Making Behavior of Maximizers and Satisficers in the Presence of Recommendations

Psychological theory distinguishes between maximizing and satisficing decision-making styles. Maximizers tend to explore more or all alternatives when making a choice, while satisficers evaluate options until they find one that is good enough. There is limited research that examines how the existence of a recommender influences the choice process and decisions of different types of decision-makers. We report the results of a controlled study, in which we monitored the choice process of participants when provided with automated recommendations and different types of additional information regarding available options. Our analyses show that none of the differences that were expected based on the literature manifested itself in the experiment. Maximizers neither inspected more items, nor invested more time to study them. Instead, like satisficers, they mostly picked one of the top-ranked items recommended by the system, which emphasizes the value of recommenders in particular for maximizers, who would otherwise face a more challenging decision problem. The analysis of the preferences of participants over different types of additional information revealed that highlighting key pros and cons was perceived as particularly helpful for the maximizers, an insight that can be used for the design of explanation approaches for recommenders.

[1]  Shou-De Lin,et al.  A modified random walk framework for handling negative ratings and generating explanations , 2013, TIST.

[2]  B. Schwartz,et al.  Maximizing Versus Satisficing : Happiness Is a Matter of Choice , 2002 .

[3]  Christopher P. Holland,et al.  The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour , 2015, Decis. Support Syst..

[4]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[5]  George Forman,et al.  A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com , 2012, ECML/PKDD.

[6]  Dietmar Jannach,et al.  Recommendations with a Purpose , 2016, RecSys.

[7]  B. Schwartz,et al.  Doing Better but Feeling Worse , 2006, Psychological science.

[8]  Bart P. Knijnenburg,et al.  Each to his own: how different users call for different interaction methods in recommender systems , 2011, RecSys '11.

[9]  Tobias Höllerer,et al.  SmallWorlds: Visualizing Social Recommendations , 2010, Comput. Graph. Forum.

[10]  T. G. Chowdhury,et al.  The time-harried shopper: Exploring the differences between maximizers and satisficers , 2009 .

[11]  Barry Smyth,et al.  Great Explanations: Opinionated Explanations for Recommendations , 2015, ICCBR.

[12]  Norbert Schwarz,et al.  The role of social comparison for maximizers and satisficers: Wanting the best or wanting to be the best? , 2015 .

[13]  Mouzhi Ge,et al.  How should I explain? A comparison of different explanation types for recommender systems , 2014, Int. J. Hum. Comput. Stud..

[14]  Carlos José Pereira de Lucena,et al.  Pattern-based Explanation for Automated Decisions , 2014, ECAI.

[15]  Dietmar Jannach,et al.  Persuasive Online-Selling in Quality and Taste Domains , 2006, EC-Web.

[16]  Alfred Kobsa,et al.  Inspectability and control in social recommenders , 2012, RecSys.

[17]  Susan T. Dumais,et al.  Using Shortlists to Support Decision Making and Improve Recommender System Performance , 2015, WWW.

[18]  Tobias Höllerer,et al.  TasteWeights: a visual interactive hybrid recommender system , 2012, RecSys.

[19]  Edward H. Shortliffe,et al.  A Framework for Explaining Decision-Theoretic Advice , 1994, Artif. Intell..

[20]  Dietmar Jannach,et al.  A case study on the effectiveness of recommendations in the mobile internet , 2009, RecSys '09.

[21]  Mark P. Graus,et al.  Understanding the role of latent feature diversification on choice difficulty and satisfaction , 2016, User Modeling and User-Adapted Interaction.

[22]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[23]  Barry Schwartz,et al.  The Maximization Paradox : The costs of seeking alternatives , 2009 .

[24]  Dietmar Jannach,et al.  A systematic review and taxonomy of explanations in decision support and recommender systems , 2017, User Modeling and User-Adapted Interaction.

[25]  Dietmar Jannach,et al.  Interacting with Recommenders—Overview and Research Directions , 2017, ACM Trans. Interact. Intell. Syst..

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