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technical-note

Workshop on human decision making in recommender systems: decisions@RecSys'13

Published: 12 October 2013 Publication History

Abstract

A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios. The submitted papers discuss a wide range of topics from core algorithmic issues to the management of the human computer interaction.

References

[1]
G. Adomavicius, J. Bockstedt, S. Curley, and J. Zhang. Recommender systems, consumer preferences, and anchoring effects. In RecSys 2011 Workshop on Human Decision Making in Recommender Systems, pages 35--42, 2011.
[2]
J. Bettman, M. Luce, and J. Payne. Constructive consumer choice processes. Journal of Consumer Research, 25(3):187--217, 1998.
[3]
D. Cosley, S. Lam, I. Albert, J. Konstan, and J. Riedl. Is seeing believing?: how recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '03, pages 585--592, New York, NY, USA, 2003. ACM.
[4]
D. Kahneman. Maps of bounded rationality: psychology for behavioral economics. The American Economic Review, 93(5):1449--1475, 2003.
[5]
M. Mandl, A. Felfernig, E. Teppan, and M. Schubert. Consumer decision making in knowledge-based recommendation. Journal of Intelligent Information Systems, 37(1):1--22, Aug. 2011.
[6]
C. Warren, A. McGraw, and L. VanBoven. Values and preferences: defining preference construction. Wiley Interdisciplinary Review: Cognitive Science, 2(2):193--205, 2010.
[7]
J. Zhang. Anchoring effects of recommender systems. In Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pages 375--378, New York, NY, USA, 2011. ACM.

Cited By

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  • (2021)The ‘Unreasonable’ Effectiveness of Graphical User Interfaces for Recommender SystemsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3461682(22-28)Online publication date: 21-Jun-2021

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Information

Published In

cover image ACM Conferences
RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
October 2013
516 pages
ISBN:9781450324090
DOI:10.1145/2507157
  • General Chairs:
  • Qiang Yang,
  • Irwin King,
  • Qing Li,
  • Program Chairs:
  • Pearl Pu,
  • George Karypis
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2013

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Author Tags

  1. decision biases
  2. decision making
  3. decision psychology
  4. recommendation algorithms

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  • Technical-note

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RecSys '13
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Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2021)The ‘Unreasonable’ Effectiveness of Graphical User Interfaces for Recommender SystemsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3461682(22-28)Online publication date: 21-Jun-2021

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