skip to main content
10.1145/2507157.2507188acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

Rating support interfaces to improve user experience and recommender accuracy

Published:12 October 2013Publication History

ABSTRACT

One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to external measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rating decisions using exemplars. In our study, we introduce interfaces that provide these methods of support. We also present a set of methodologies to evaluate the efficacy of the new interfaces via a user experiment. Our results suggest that presenting exemplars during the rating process helps users rate more consistently, and increases the quality of the data.

References

  1. G. Adomavicius, J. Bockstedt, S. Curley, and J. Zhang. Recommender systems, consumer preferences, and anchoring effects. InDecisions@RecSys Workshop, pages 35--42, Chicago, 2011.Google ScholarGoogle Scholar
  2. X. Amatriain, J. M. Pujol, N. Tintarev, and N. Oliver. Rate it again: increasing recommendation accuracy by user re-rating. In Proc. of RecSys '09, pages 173--180, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. R. Bettman, M. F. Luce, and J. W. Payne. Constructive consumer choice processes. Journal of Consumer Research, 25(3):187 -- 217, Dec. 1998. ArticleType: research-article /Full publication date: December 1998 / Copyright 1998 Journal of Consumer Research Inc.Google ScholarGoogle ScholarCross RefCross Ref
  4. D. Bollen, M. Graus, and M. C. Willemsen. Remembering the stars': effect of time on preference retrieval from memory. In Proc. of RecSys '12, pages 217 -- 220, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Cosley, S. K. Lam, I. Albert, J. A. Konstan, and J. Riedl. Is seeing believing?: how recommender system interfaces affect users' opinions. In In Proc. of CHI '03, pages 585--592, New York, NY, USA, 2003. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. D. Ekstrand, M. Ludwig, J. A. Konstan, and J. T. Riedl. Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In In Proc. of RecSys '11, pages 133 -- 140, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. B. Fischho. Value elicitation: Is there anything in there? American Psychologist, 46(8):835--847, 1991.Google ScholarGoogle ScholarCross RefCross Ref
  8. F. M. Harper, X. Li, Y. Chen, and J. A. Konstan. An economic model of user rating in an online recommender 7 http://www.net ixprize.com system. In User Modeling 2005, pages 307--316. Springer, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5--53, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. W. Hill, L. Stead, M. Rosenstein, and G. Furnas. Recommending and evaluating choices in a virtual community of use. In In Proc. of CHI '95, pages 194--201, New York, NY, USA, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C. K. Hsee. The evaluability hypothesis: An explanation for preference reversals between joint and separate evaluations of alternatives. Organizational Behavior and Human Decision Processes, 67(3):247--257, Sept. 1996.Google ScholarGoogle ScholarCross RefCross Ref
  12. L.-t. Hu and P. Bentler. Cuto criteria for t indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1):1--55, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  13. D. Kluver, T. T. Nguyen, M. Ekstrand, S. Sen, and J. Riedl. How many bits per rating? In In Proc. of RecSys '12, pages 99 -- 106, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. B. P. Knijnenburg, M. C. Willemsen, Z. Gantner, H. Soncu, and C. Newell. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4-5):441--504, Mar. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Lichtenstein and P. Slovic. The Construction of Preference. Cambridge University Press, Sept. 2006.Google ScholarGoogle ScholarCross RefCross Ref
  16. S. M. McNee, S. K. Lam, J. A. Konstan, and J. Riedl. Interfaces for eliciting new user preferences in recommender systems. In P. Brusilovsky, A. Corbett, and F. d. Rosis, editors, User Modeling 2003, number 2702 in Lecture Notes in Computer Science, pages 178--187. Springer Berlin Heidelberg, Jan. 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. Mussweiler. Comparison processes in social judgment: Mechanisms and consequences. Psychological Review, 110(3):472--489, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  18. T. T. Nguyen and J. Riedl. Predicting users' preference from tag relevance. User Modeling, Adaptation, and Personalization, UMAP 2013, pages 274--280, 2013.Google ScholarGoogle Scholar
  19. S. Nobarany, L. Oram, V. K. Rajendran, C.-H. Chen, J. McGrenere, and T. Munzner. The design space of opinion measurement interfaces: exploring recall support for rating and ranking. In Proc. of CHI '12, pages 2035 -- 2044, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. P. O'Mahony, N. J. Hurley, and G. C. Silvestre. Detecting noise in recommender system databases. In Proceedings of the 11th international conference on Intelligent user interfaces, IUI '06, pages 109 -- 115, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. Said, B. J. Jain, S. Narr, and T. Plumbaum. Users and noise: The magic barrier of recommender systems. In UMAP 2012, pages 237--248. Springer, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. I. Simonson. Determinants of customers' responses to customized offers: Conceptual framework and research propositions. Journal of Marketing, 69(1):32--45, Jan. 2005.Google ScholarGoogle ScholarCross RefCross Ref
  23. E. I. Sparling and S. Sen. Rating: how difficult is it? In Proc. of RecSys '11, pages 149 -- 156, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Tversky and D. Kahneman. Judgment under uncertainty: Heuristics and biases. Science, 185(4157):1124--1131, Sept. 1974.Google ScholarGoogle ScholarCross RefCross Ref
  25. A. Tversky, S. Sattath, and P. Slovic. Contingent weighting in judgment and choice. Psychological Review, 95(3):371--384, 1988.Google ScholarGoogle ScholarCross RefCross Ref
  26. J. Vig, S. Sen, and J. Riedl. The tag genome: Encoding community knowledge to support novel interaction. ACM Trans. Interact. Intell. Syst., 2(3):13:1 -- 13:44, Sept. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. E. U. Weber and E. J. Johnson. Mindful judgment and decision making. In Annual Review of Psychology, volume 60, pages 53--85. Annual Reviews, Palo Alto, 2009.Google ScholarGoogle Scholar

Index Terms

  1. Rating support interfaces to improve user experience and recommender accuracy

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • 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

      Copyright © 2013 ACM

      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 October 2013

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      RecSys '13 Paper Acceptance Rate32of136submissions,24%Overall Acceptance Rate254of1,295submissions,20%

      Upcoming Conference

      RecSys '24
      18th ACM Conference on Recommender Systems
      October 14 - 18, 2024
      Bari , Italy

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader