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Crafting the initial user experience to achieve community goals

Published:23 October 2008Publication History

ABSTRACT

Recommender systems try to address the "new user problem" by quickly and painlessly learning user preferences so that users can begin receiving recommendations as soon as possible. We take an expanded perspective on the new user experience, seeing it as an opportunity to elicit valuable contributions to the community and shape subsequent user behavior. We conducted a field experiment in MovieLens where we imposed additional work on new users: not only did they have to rate movies, they also had to enter varying numbers of tags. While requiring more work led to fewer users completing the entry process, the benefits were significant: the remaining users produced a large volume of tags initially, and continued to enter tags at a much higher rate than a control group. Further, their rating behavior was not depressed. Our results suggest that careful design of the initial user experience can lead to significant benefits for an online community.

References

  1. E. Aronson and J. Mills. The effect of severity of initiation on liking for a group. Journal of Abnormal and Social Psychology, 59:177--181, 1959.Google ScholarGoogle ScholarCross RefCross Ref
  2. G. Beenen, K. Ling, K. Ling, X. Wang, K. Chang, D. Frankowski, P. Resnick, and R. E. Kraut. Using social psychology to motivate contributions to online communities. In Proceedings of the 2004 ACM conference on Computer supported cooperative work, pages 212--221, New York, NY, USA, 2004. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. E. Clary, M. Snyder, R. Ridge, J. Copeland, A. Stukas, J. Haugen, and P. Miene. Understanding and assessing the motivations of volunteers: A functional approach. Journal of Personality and Social Psychology, 74:1516--1530, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Claypool, P. Le, M. Wased, and D. Brown. Implicit interest indicators. In Proceedings of the 6th international conference on Intelligent user interfaces, pages 33--40, New York, NY, USA, 2001. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Cosley, D. Frankowski, S. Kiesler, L. Terveen, and J. Riedl. How oversight improves member-maintained communities. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 11--20, New York, NY, USA, 2005. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Cosley, D. Frankowski, L. Terveen, and J. Riedl. Using intelligent task routing and contribution review to help communities build artifacts of lasting value. In Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 1037--1046, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Cosley, D. Frankowski, L. Terveen, and J. Riedl. Suggestbot: using intelligent task routing to help people find work in wikipedia. In Proceedings of the 12th international conference on Intelligent user interfaces, pages 32--41, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. Drenner, M. Harper, D. Frankowski, J. Riedl, and L. Terveen. Insert movie reference here: a system to bridge conversation and item-oriented web sites. In Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 951--954, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Festinger and J. Carlsmith. Cognitive consequences of forced compliance. Journal of Abnormal and Social Psychology.Google ScholarGoogle Scholar
  10. A. Hars and S. Ou. Working for free? - motivations of participating in open source projects. In Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 7, page 7014, Washington, DC, USA, 2001. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. K. Lam and J. Riedl. Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web, pages 393--402, New York, NY, USA, 2004. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. C. A. Lampe, E. Johnston, E. Johnston, and P. Resnick. Follow the reader: filtering comments on slashdot. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 1253--1262, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. E. A. Locke and G. P. Latham. Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9):705--717, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  14. O. Nov. What motivates wikipedians? Commun. ACM, 50(11):60--64, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. M. Rashid, I. Albert, D. Cosley, S. K. Lam, S. M. McNee, J. A. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent user interfaces, pages 127--134, New York, NY, USA, 2002. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Sen, F. M. Harper, A. LaPitz, and J. Riedl. The quest for quality tags. In Proceedings of the 2007 international ACM conference on Supporting group work, pages 361--370, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Sen, S. K. Lam, A. M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. M. Harper, and J. Riedl. tagging, communities, vocabulary, evolution. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pages 181--190, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. P. Viappiani, P. Pu, and B. Faltings. Conversational recommenders with adaptive suggestions. In Proceedings of the 2007 ACM conference on Recommender systems, pages 89--96, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
      October 2008
      348 pages
      ISBN:9781605580937
      DOI:10.1145/1454008

      Copyright © 2008 ACM

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      Publication History

      • Published: 23 October 2008

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