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How Users Perceive and Appraise Personalized Recommendations

  • Nicolas Jones
  • Pearl Pu
  • Li Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)

Abstract

Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone as far as using implicit feedback indicators to understand users’ interests. More than a decade after the emergence of recommender systems, the question whether users prefer them compared to stating their preferences explicitly, largely remains a subject of study. Even though some studies were found on users’ acceptance and perceptions of this technology, these were general marketing-oriented surveys. In this paper we report an in-depth user study comparing Amazon’s implicit book recommender with a baseline model of explicit search and browse. We address not only the question “do people accept recommender systems” but also how or under what circumstances they do and more importantly, what can still be improved.

Keywords

Recommender System Collaborative Filter User Perceive Recommendation Quality Implicit Rating 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Choicestream personalization survey (2007), http://www.choicestream.com/
  2. 2.
    Chen, L., Pu, P.: Evaluating critiquing-based recommender agents. In: AAAI (2006)Google Scholar
  3. 3.
    Claypool, M., Claypool, M., Brown, D., Brown, D., Le, P., Le, P., Waseda, M., Waseda, M.: Inferring user interest. IEEE Internet Computing 5, 32–39 (2001)CrossRefGoogle Scholar
  4. 4.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35, 61–70 (1992)CrossRefGoogle Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  6. 6.
    Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley series in probability and mathematical statistics. John Wiley & Sons, Inc., New York (1976)zbMATHGoogle Scholar
  7. 7.
    Nichols, D.M.: Implicit rating and filtering. In: Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36 (1997)Google Scholar
  8. 8.
    Pu, P., Chen, L.: User-involved preference elicitation for product search and recommender systems. AI Magazine 29(4), 93–103 (2008)CrossRefGoogle Scholar
  9. 9.
    Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of ACM CSCW 2004, pp. 175–186. ACM, New York (2004)Google Scholar
  10. 10.
    Shapira, B., Taieb-Maimon, M., Moskowitz, A.: Study of the usefulness of known and new implicit indicators and their optimal combination for accurate inference of users interests. In: SAC 2006, ACM, New York (2006)Google Scholar
  11. 11.
    Shneiderman, B., Maes, P.: Direct manipulation vs. interface agents. Interactions 4, 6 (1997)CrossRefGoogle Scholar
  12. 12.
    Swearingen, K., Sinha, R.: Beyond algorithms: An hci perspective on recommender systems (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nicolas Jones
    • 1
  • Pearl Pu
    • 1
  • Li Chen
    • 2
  1. 1.Human Computer Interaction GroupSwiss Federal Institute of TechnologySwitzerland
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong Kong

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