User Groups and Different Levels of Control in Recommender Systems

  • Christine Mendez
  • Vlatko Lukarov
  • Christoph Greven
  • André Calero ValdezEmail author
  • Felix Dietze
  • Ulrik Schroeder
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10286)


The aspect of control in recommender systems has already been extensively researched in the past. Quite a number of studies performed by various researchers reported that an increase in control had a positive effect for example on user satisfaction with a system, or recommendation accuracy. Recent studies investigated whether this positive effect of control applies to all users, or finer distinctions have to be made between different user groups, which in turn require different levels of control. Those studies identified several characteristics, along which users could be divided into groups: expertise in recommender systems, domain knowledge, trusting propensity, persistence. They reported different needs of control for different user groups. However, the effect of those characteristics has not been systematically examined with regard to all three recommendation phases introduced earlier by Pu and Zhang, namely initial preference elicitation, preference refinement, result display. This paper suggests, that for different levels of expertise and trust, different levels of control are necessary during preference elicitation, whereas persistence does not play a prevalent role in this phase. Further assumptions are made for preference refinement and result display. In addition to the three phases, context, type of information required and visualization of control methods are identified as factors influencing the request of users for control.


Recommender systems User groups Controllability User satisfaction 



The authors thank the German Research Council DFG for the friendly support of the research in the excellence cluster “Integrative Production Technology in High Wage Countries”.


  1. 1.
    Bostandjiev, S., O’Donovan, J., Höllerer, T.: TasteWeights: a visual interactive hybrid recommender system. In: Proceedings of the Sixth ACM Conference on Recommender Systems, Dublin, Ireland, 09–13 September 2012, RecSys 2012, pp. 35–42. ACM, New York.
  2. 2.
    Chen, L., Pu, P.: Evaluating critiquing-based recommender agents. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, p. 157. AAAI Press/MIT Press, London/Cambridge (1999, 2006)Google Scholar
  3. 3.
    Ekstrand, M.D., Kluver, D., Harper, F.M., Konstan, J.A.: Letting users choose recommender algorithms: an experimental study. In: Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria, 16–20 September 2015, RecSys 2015, pp. 11–18. ACM, New York (2015).
  4. 4.
    Gretarsson, B., O’Donovan, J., Bostandjiev, S., Hall, C., Höllerer, T.: Smallworlds: visualizing social recommendations. Comput. Graph. Forum 29(3), 833–842 (2010). Blackwell. CrossRefGoogle Scholar
  5. 5.
    Harper, F.M, Xu, F., Kaur, H., Condiff, K., Chang, S., Terveen, L.: Putting users in control of their recommendations. In: Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria, 16–20 September 2015, RecSys 2015, pp. 3–10. ACM, New York (2015).
  6. 6.
    Hijikata, Y., Kai, Y., Nishida, S.: The relation between user intervention and user satisfaction for information recommendation. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, Trento, Italy, 26–30 March 2012, SAC 2012, pp. 2002–2007. ACM, New York (2012).
  7. 7.
    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
  8. 8.
    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: Proceedings of the Fifth ACM Conference on Recommender Systems, Chicago, Illinois, USA, 23–27 October 2011, RecSys 2011, pp. 141–148. ACM, New York (2011).
  9. 9.
    Knijnenburg, B.P., Bostandjiev, S., O’Donovan, J., Kobsa, A.: Inspectability and control in social recommenders. In: Proceedings of the Sixth ACM Conference on Recommender Systems, Dublin, Ireland, 09–13 September 2012, RecSys 2012, pp. 43–50. ACM, New York (2012).
  10. 10.
    Loepp, B., Hussein, T., Ziegler, J.: Choice-based preference elicitation for collaborative filtering recommender systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, Ontario, Canada, 26 April–01 May 2014, CHI 2014, pp. 3085–3094. ACM, New York (2014).
  11. 11.
    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
  12. 12.
    Parra, D., Brusilovsky, P.: User-controllable personalization. A case study with SetFusion. Int. J. Hum.-Comput. Stud. 78, 43–67 (2015). doi: 10.1016/j.ijhcs.2015.01.007. ElsevierCrossRefGoogle Scholar
  13. 13.
    Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective. Survey of the state of the art. User Model. User-Adapt. Interact. 22(4–5), 317–355 (2012). doi: 10.1007/s11257-011-9115-7. SpringerCrossRefGoogle Scholar
  14. 14.
    Verbert, K., Parra, D., Brusilovsky, P., Duval, E.: Visualizing recommendations to support exploration, transparency and controllability. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, Santa Monica, California, USA, 19–22 March 2013, IUI 2013, pp. 351–362. ACM, New York (2013).
  15. 15.
    Sweller, J.: Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ. Psychol. Rev. 22(2), 123–138 (2010). doi: 10.1007/s10648-010-9128-5. Springer USCrossRefGoogle Scholar
  16. 16.
    Valdez, A.C., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, Massachusetts, USA, 15–19 September 2016, RecSys 2016, pp. 123–126. ACM, New York (2016).

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christine Mendez
    • 1
  • Vlatko Lukarov
    • 1
  • Christoph Greven
    • 1
  • André Calero Valdez
    • 2
    Email author
  • Felix Dietze
    • 2
  • Ulrik Schroeder
    • 1
  • Martina Ziefle
    • 2
  1. 1.Learning Technologies Research GroupRWTH Aachen UniversityAachenGermany
  2. 2.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

Personalised recommendations