Decision making strategies differ in the presence of collaborative explanations: two conjoint studies
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Panagiotis Symeonidis | Markus Zanker | Laurens Rook | Ludovik Coba | M. Zanker | Ludovik Çoba | L. Rook | P. Symeonidis
[1] P. Todd,et al. Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload , 2010 .
[2] Kun-Pyo Lee,et al. The Elders Preference for Skeuomorphism as App Icon Style , 2015, CHI Extended Abstracts.
[3] Kyung Hyan Yoo,et al. Persuasive Recommender Systems - Conceptual Background and Implications , 2012, Springer Briefs in Electrical and Computer Engineering.
[4] Raymond J. Mooney,et al. Explaining Recommendations: Satisfaction vs. Promotion , 2005 .
[5] Lise Getoor,et al. User Preferences for Hybrid Explanations , 2017, RecSys.
[6] Deborah Marshall,et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[7] Barry Schwartz,et al. The Maximization Paradox : The costs of seeking alternatives , 2009 .
[8] Dietmar Jannach,et al. A systematic review and taxonomy of explanations in decision support and recommender systems , 2017, User Modeling and User-Adapted Interaction.
[9] John R. Hauser,et al. Conjoint Analysis, Related Modeling, and Applications , 2004 .
[10] Panagiotis Symeonidis,et al. Exploring Users' Perception of Collaborative Explanation Styles , 2018, 2018 IEEE 20th Conference on Business Informatics (CBI).
[11] Dietmar Jannach,et al. Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation , 2015, i-com.
[12] Lisa A Prosser,et al. Statistical Methods for the Analysis of Discrete-Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force. , 2016, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[13] Markus Zanker,et al. Decision Biases in Recommender Systems , 2015 .
[14] John Riedl,et al. Explaining collaborative filtering recommendations , 2000, CSCW '00.
[15] Jerry Wind,et al. Courtyard by Marriott: Designing a Hotel Facility with Consumer-Based Marketing Models , 1989 .
[16] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[17] Bin Gu,et al. Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..
[18] Li Chen,et al. Human Decision Making and Recommender Systems , 2015, Recommender Systems Handbook.
[19] Panagiotis Symeonidis,et al. Exploring Users' Perception of Rating Summary Statistics , 2018, UMAP.
[20] Rick Dale,et al. Good things peak in pairs: a note on the bimodality coefficient , 2013, Front. Psychol..
[21] M. Zanker,et al. An empirical study on the persuasiveness of fact-based explanations for recommender systems , 2014 .
[22] Markus Zanker,et al. Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain , 2012, EC-Web.
[23] Gergana Y. Nenkov,et al. A short form of the Maximization Scale: Factor structure, reliability and validity studies , 2008, Judgment and Decision Making.
[24] L. Salmaso,et al. Permutation tests for complex data : theory, applications and software , 2010 .
[25] Judith Masthoff,et al. Explaining Recommendations: Design and Evaluation , 2015, Recommender Systems Handbook.
[26] W. Greene,et al. 计量经济分析 = Econometric analysis , 2009 .
[27] Luigi Salmaso,et al. Permutation Tests for Complex Data , 2010 .
[28] John W. Payne,et al. Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .
[29] Peter Brusilovsky,et al. User modeling and user adapted interaction , 2001 .
[30] Maarten J. IJzerman,et al. Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force. , 2016, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[31] Dietmar Jannach,et al. Investigating the Decision-Making Behavior of Maximizers and Satisficers in the Presence of Recommendations , 2018, UMAP.
[32] B. Schwartz,et al. Doing Better but Feeling Worse , 2006, Psychological science.
[33] Matthias Brand,et al. Choosing a Physician on Social Media: Comments and Ratings of Users are More Important than the Qualification of a Physician , 2018, Int. J. Hum. Comput. Interact..
[34] Alan Kennedy,et al. Book Review: Eye Tracking: A Comprehensive Guide to Methods and Measures , 2016, Quarterly journal of experimental psychology.
[35] Olfa Nasraoui,et al. Using Explainability for Constrained Matrix Factorization , 2017, RecSys.
[36] V. Rao,et al. A General Consumer Preference Model for Experience Products: Application to Internet Recommendation Services , 2012 .
[37] Jeff Sauro,et al. Average task times in usability tests: what to report? , 2010, CHI 2010.
[38] Vithala R. Rao,et al. Developments in Conjoint Analysis , 2008 .
[39] Alexander Felfernig,et al. Minimization of decoy effects in recommender result sets , 2012, Web Intell. Agent Syst..
[40] B. Schwartz,et al. Maximizing versus satisficing: happiness is a matter of choice , 2002 .
[41] Bart P. Knijnenburg,et al. Each to his own: how different users call for different interaction methods in recommender systems , 2011, RecSys '11.
[42] Ursina Teuscher,et al. Time flies when you maximize - maximizers and satisficers perceive time differently when making decisions. , 2013, Acta psychologica.
[43] Bart de Langhe,et al. Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings , 2016 .
[44] Richard P. Eibach,et al. Failing to commit: Maximizers avoid commitment in a way that contributes to reduced satisfaction , 2012 .
[45] V. Rao,et al. An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research , 2015 .
[46] Paul A. Pavlou,et al. Overcoming the J-shaped distribution of product reviews , 2009, CACM.
[47] B. Schwartz,et al. Maximizing Versus Satisficing : Happiness Is a Matter of Choice , 2002 .
[48] H. Simon,et al. A Behavioral Model of Rational Choice , 1955 .
[49] Luigi Salmaso,et al. The importance of landscape in wine quality perception: An integrated approach using choice-based conjoint analysis and combination-based permutation tests , 2010 .
[50] J. Louviere,et al. Discrete Choice Experiments Are Not Conjoint Analysis , 2010 .
[51] Markus Zanker,et al. Decision Making Based on Bimodal Rating Summary Statistics - An Eye-Tracking Study of Hotels , 2018, ENTER.
[52] Joel Huber,et al. A General Method for Constructing Efficient Choice Designs , 1996 .
[53] Gerhard Friedrich,et al. A Taxonomy for Generating Explanations in Recommender Systems , 2011, AI Mag..
[54] Barry Schwartz,et al. On the meaning and measurement of maximization , 2016, Judgment and Decision Making.
[55] John Riedl,et al. Is seeing believing?: how recommender system interfaces affect users' opinions , 2003, CHI '03.
[56] Vithala R. Rao,et al. Choice Based Conjoint Studies: Design and Analysis , 2014 .