skip to main content
10.1145/3025171.3025209acmconferencesArticle/Chapter ViewAbstractPublication PagesiuiConference Proceedingsconference-collections
research-article

How to Recommend?: User Trust Factors in Movie Recommender Systems

Published:07 March 2017Publication History

ABSTRACT

How much trust a user places in a recommender is crucial to the uptake of the recommendations. Although prior work established various factors that build and sustain user trust, their comparative impact has not been studied in depth. This paper presents the results of a crowdsourced study examining the impact of various recommendation interfaces and content selection strategies on user trust. It evaluates the subjective ranking of nine key factors of trust grouped into three dimensions and examines the differences observed with respect to users' personality traits.

References

  1. I. Benbasat and W. Wang. Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 2005.Google ScholarGoogle ScholarCross RefCross Ref
  2. Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), pages 289--300, 1995.Google ScholarGoogle ScholarCross RefCross Ref
  3. L. M. Collins, J. J. Dziak, and R. Li. Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psychological methods, 14(3):202, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  4. P. T. Costa and R. R. McCrae. Four ways five factors are basic. Personality and individual differences, 13(6):653--665, 1992.Google ScholarGoogle Scholar
  5. H. S. M. Cramer, V. Evers, S. Ramlal, M. van Someren, L. Rutledge, N. Stash, L. Aroyo, and B. J. Wielinga. The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction, 18(5):455--496, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Dillon and C. Watson. User analysis in HCI the historical lessons from individual differences research. International Journal of Human-Computer Studies, 45(6):619--637, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Felfernig and B. Gula. An empirical study on consumer behavior in the interaction with knowledge-based recommender applications. In Proceedings of the International Conference on E-Commerce Technology, CEC, page 37, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. N. Finnerty, B. Lepri, and F. Pianesi. Acquisition of personality. In Emotions and Personality in Personalized Services, pages 81--99. 2016.Google ScholarGoogle ScholarCross RefCross Ref
  9. S. D. Gosling, P. J. Rentfrow, and W. B. Swann. A very brief measure of the big-five personality domains. Journal of Research in personality, 37(6):504--528, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  10. A. Gunawardana and G. Shani. Evaluating recommender systems. In Recommender Systems Handbook, pages 265--308. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  11. K. A. Hoff and M. Bashir. Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3):407--434, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  12. D. Holliday, S. M. Wilson, and S. Stumpf. User trust in intelligent systems: A journey over time. In Proceedings of the International Conference on Intelligent User Interfaces, IUI, pages 164--168, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Jameson, M. C. Willemsen, A. Felfernig, M. de Gemmis, P. Lops, G. Semeraro, and L. Chen. Human decision making and recommender systems. In Recommender Systems Handbook, pages 611--648. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. D. Johnson, J. Sanchez, A. D. Fisk, and W. A. Rogers. Type of automation failure: The effects on trust and reliance in automation. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 48, pages 2163--2167, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  15. S. Y. Komiak and I. Benbasat. The effects of personalization and familiarity on trust and adoption of recommendation agents. Management Information Systems Quarterly, pages 941--960, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. D. Lee and N. Moray. Trust, self-confidence, and operators' adaptation to automation. International Journal of Human-Computer Studies, 40(1):153--184, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. D. Lee and K. A. See. Trust in automation: Designing for appropriate reliance. Human Factors, 46(1):50--80, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  18. S. Matz, Y. W. F. Chan, and M. Kosinski. Models of personality. In Emotions and Personality in Personalized Services, pages 35--54. 2016.Google ScholarGoogle ScholarCross RefCross Ref
  19. N. Moray, T. Inagaki, and M. Itoh. Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. Journal of Experimental Psychology: Applied, 6(1):44, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  20. S. Nowak and S. M. Rüger. How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In Proceedings of the International Conference on Multimedia Information Retrieval, MIR, pages 557--566, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. O'Donovan and B. Smyth. Trust in recommender systems. In Proceedings of the International Conference on Intelligent User Interfaces, IUI, pages 167--174, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. U. Panniello, M. Gorgoglione, and A. Tuzhilin. Research note In CARSs we trust: How context-aware recommendations affect customers' trust and other business performance measures of recommender systems. Information Systems Research, 27(1):182--196, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  23. P. Pu and L. Chen. Trust building with explanation interfaces. In Proceedings of the International Conference on Intelligent User Interfaces, IUI, pages 93--100, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. P. Pu, L. Chen, and R. Hu. A user-centric evaluation framework for recommender systems. In Proceedings of the ACM Conference on Recommender Systems, RecSys, pages 157--164, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. W. B. Rouse. Adaptive aiding for human/computer control. Human Factors, 30(4):431--443, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. A. Said, S. Berkovsky, E. W. D. Luca, and J. Hermanns. Challenge on context-aware movie recommendation: Camra2011. In Proceedings of the ACM Conference on Recommender Systems, RecSys, pages 385--386, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. J. Sauer, A. Chavaillaz, and D. Wastell. Experience of automation failures in training: effects on trust, automation bias, complacency and performance. Ergonomics, pages 1--14, 2015.Google ScholarGoogle Scholar
  28. K. E. Schaefer and D. R. Scribner. Individual differences, trust, and vehicle autonomy a pilot study. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 59, pages 786--790, 2015.Google ScholarGoogle Scholar
  29. J. Schrammel, C. Köffel, and M. Tscheligi. Personality traits, usage patterns and information disclosure in online communities. In Proceedings of the British Computer Society Conference on Human-Computer Interaction, BCS-HCI, pages 169--174, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. C. L. Scott. Interpersonal trust: A comparison of attitudinal and situational factors. Human Relations, 33(11):805--812, 1980.Google ScholarGoogle ScholarCross RefCross Ref
  31. G. Shani, L. Rokach, B. Shapira, S. Hadash, and M. Tangi. Investigating confidence displays for top-N recommendations. Journal of the Association for Information Science and Technology, 64(12):2548--2563, 2013.Google ScholarGoogle Scholar
  32. R. R. Sinha and K. Swearingen. Comparing recommendations made by online systems and friends. In Proceedings of the Workshop on Personalisation and Recommender Systems in Digital Libraries, 2001.Google ScholarGoogle Scholar
  33. K. Swearingen and R. Sinha. Interaction design for recommender systems. In Designing Interactive Systems, volume 6, pages 312--334, 2002.Google ScholarGoogle Scholar
  34. N. Tintarev and J. Masthoff. Explaining recommendations: Design and evaluation. In Recommender Systems Handbook, pages 353--382. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  35. M. Tkalcic and L. Chen. Personality and recommender systems. In Recommender Systems Handbook, pages 715--739. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  36. P. Victor, M. D. Cock, and C. Cornelis. Trust and recommendations. In Recommender Systems Handbook, pages 645--675. 2011.Google ScholarGoogle ScholarCross RefCross Ref
  37. A. Vinciarelli and G. Mohammadi. A survey of personality computing. IEEE Transactions on Affective Computing, 5(3):273--291, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  38. Y. D. Wang and H. H. Emurian. An overview of online trust: Concepts, elements, and implications. Computers in Human Behavior, 21(1):105--125, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  39. W. Wu, L. Chen, and L. He. Using personality to adjust diversity in recommender systems. In Proceedings of the Conference on Hypertext and Social Media, HT, pages 225--229, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. S. Xiao and I. Benbasat. The formation of trust and distrust in recommendation agents in repeated interactions: a process-tracing analysis. In Proceedings of the International Conference on Electronic Commerce, ICEC, pages 287--293, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. K. H. Yoo, U. Gretzel, and M. Zanker. Source factors in recommender system credibility evaluation. In Recommender Systems Handbook, pages 689--714. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  42. K. Yu, S. Berkovsky, D. Conway, R. Taib, J. Zhou, and F. Chen. Trust and reliance based on system accuracy. In Proceedings of the Conference on User Modeling Adaptation and Personalization, UMAP, pages 223--227, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. K. Yu, S. Berkovsky, R. Taib, D. Conway, J. Zhou, and F. Chen. User trust dynamics: An investigation driven by differences in system performance. In Proceedings of the International Conference on Intelligent User Interfaces, IUI, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. How to Recommend?: User Trust Factors in Movie Recommender Systems

    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
      IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces
      March 2017
      654 pages
      ISBN:9781450343480
      DOI:10.1145/3025171

      Copyright © 2017 ACM

      © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 March 2017

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      IUI '17 Paper Acceptance Rate63of272submissions,23%Overall Acceptance Rate746of2,811submissions,27%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader