User Control in Recommender Systems: Overview and Interaction Challenges

  • Dietmar Jannach
  • Sidra Naveed
  • Michael Jugovac
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 278)


Recommender systems have shown to be valuable tools that help users find items of interest in situations of information overload. These systems usually predict the relevance of each item for the individual user based on their past preferences and their observed behavior. If the system’s assumption about the users’ preferences are however incorrect or outdated, mechanisms should be provided that put the user into control of the recommendations, e.g., by letting them specify their preferences explicitly or by allowing them to give feedback on the recommendations. In this paper we review and classify the different approaches from the research literature of putting the users into active control of what is recommended. We highlight the challenges related to the design of the corresponding user interaction mechanisms and finally present the results of a survey-based study in which we gathered user feedback on the implemented user control features on Amazon.


  1. 1.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM 2008, pp. 263–272 (2008)Google Scholar
  2. 2.
    Amatriain, X., Pujol, J.M., Tintarev, N., Oliver, N.: Rate it again: increasing recommendation accuracy by user re-rating. In: RecSys 2009, pp. 173–180 (2009)Google Scholar
  3. 3.
    Knijnenburg, B.P., Bostandjiev, S., O’Donovan, J., Kobsa, A.: Inspectability and control in social recommenders. In: RecSys 2012, pp. 43–50 (2012)Google Scholar
  4. 4.
    Dooms, S., De Pessemier, T., Martens, L.: Improving IMDb movie recommendations with interactive settings and filter. In: RecSys 2014 (2014)Google Scholar
  5. 5.
    McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: Brusilovsky, P., Corbett, A., Rosis, F. (eds.) UM 2003. LNCS (LNAI), vol. 2702, pp. 178–187. Springer, Heidelberg (2003). doi: 10.1007/3-540-44963-9_24 CrossRefGoogle Scholar
  6. 6.
    Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31(1), 137–209 (2007)Google Scholar
  7. 7.
    Hijikata, Y., Kai, Y., Nishida, S.: The relation between user intervention and user satisfaction for information recommendation. In: SAC 2012, pp. 2002–2007. (2012)Google Scholar
  8. 8.
    Wasinger, R., Wallbank, J., Pizzato, L., Kay, J., Kummerfeld, B., Böhmer, M., Krüger, A.: Scrutable user models and personalised item recommendation in mobile lifestyle applications. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 77–88. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38844-6_7 CrossRefGoogle Scholar
  9. 9.
    Knijnenburg, B.P., Reijmer, N.J., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: RecSys 2011, pp. 141–148 (2011)Google Scholar
  10. 10.
    Goker, M., Thompson, C.: The adaptive place advisor: a conversational recommendation system. In: 8th German Workshop on CBR, pp. 187–198 (2000)Google Scholar
  11. 11.
    Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. Knowl. Eng. Rev. 16(2), 111–155 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: An integrated environment for the development of knowledge-based recommender applications. Int. J. Electron. Commer. 11(2), 11–34 (2006)CrossRefGoogle Scholar
  13. 13.
    Burke, R.D., Hammond, K.J., Young, B.C.: Knowledge-based navigation of complex information spaces. In: AAAI 1996, pp. 462–468 (1996)Google Scholar
  14. 14.
    Trewin, S.: Knowledge-based recommender systems. Encyclopedia Libr. Inf. Sci. 69, 180–200 (2000)Google Scholar
  15. 15.
    Swearingen, K., Sinha, R.: Beyond algorithms: an HCI perspective on recommender systems. In: ACM SIGIR Recommender Systems Workshop, pp. 1–11 (2001)Google Scholar
  16. 16.
    Schafer, J.B., Konstan, J.A., Riedl, J.: Meta-recommendation systems: user-controlled integration of diverse recommendations. In: CIKM 2002, pp. 43–51 (2002)Google Scholar
  17. 17.
    Schaffer, J., Höllerer, T., O’Donovan, J.: Hypothetical recommendation: a study of interactive profile manipulation behavior for recommender systems. In: FLAIRS 2015, pp. 507–512 (2015)Google Scholar
  18. 18.
    Bostandjiev, S., O’Donovan, J., Höllerer, T.: Tasteweights: a visual interactive hybrid recommender system. In: RecSys 2012, pp. 35–42 (2012)Google Scholar
  19. 19.
    Tintarev, N., Kang, B., Höllerer, T., O’Donovan, J.: Inspection mechanisms for community-based content discovery in microblogs. In: RecSys IntRS 2015 Workshop, pp. 21–28 (2015)Google Scholar
  20. 20.
    Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: IEEE ICDEW Data Engineering Workshop, pp. 801–810 (2007)Google Scholar
  21. 21.
    Jannach, D., Kreutler, G.: Rapid development of knowledge-based conversational recommender applications with Advisor Suite. J. Web Eng. 6(2), 165–192 (2007)Google Scholar
  22. 22.
    Lamche, B., Adıgüzel, U., Wörndl, W.: Interactive explanations in mobile shopping recommender systems. In: RecSys IntRS 2014 Workshop, pp. 14–21 (2014)Google Scholar
  23. 23.
    Czarkowski, M., Kay, J.: A scrutable adaptive hypertext. In: Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 384–387. Springer, Heidelberg (2002). doi: 10.1007/3-540-47952-X_43 CrossRefGoogle Scholar
  24. 24.
    Ekstrand, M.D., Kluver, D., Harper, F.M., Konstan, J.A.: Letting users choose recommender algorithms: an experimental study. In: RecSys 2015, pp. 11–18 (2015)Google Scholar
  25. 25.
    Parra, D., Brusilovsky, P., Trattner, C.: See what you want to see: visual user-driven approach for hybrid recommendation. In: IUI 2014, pp. 235–240 (2014)Google Scholar
  26. 26.
    Harper, F.M., Xu, F., Kaur, H., Condiff, K., Chang, S., Terveen, L.G.: Putting users in control of their recommendations. In: RecSys 2015, pp. 3–10 (2015)Google Scholar
  27. 27.
    Jameson, A., Schwarzkopf, E.: Pros and cons of controllability: an empirical study. In: Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 193–202. Springer, Heidelberg (2002). doi: 10.1007/3-540-47952-X_21 CrossRefGoogle Scholar
  28. 28.
    Kramer, T.: The effect of measurement task transparency on preference construction and evaluations of personalized recommendations. J. Mark. Res. 44(2), 224–233 (2007)CrossRefGoogle Scholar
  29. 29.
    Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User Adapt. Interact. 22(1–2), 125–150 (2012)CrossRefGoogle Scholar
  30. 30.
    Groh, G., Birnkammerer, S., Köllhofer, V.: Social recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems for the Social Web, pp. 3–42. Springer, New York (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dietmar Jannach
    • 1
  • Sidra Naveed
    • 1
  • Michael Jugovac
    • 1
  1. 1.Department of Computer ScienceTU DortmundDortmundGermany

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