Walk the Talk

Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation
  • Denis Parra
  • Xavier Amatriain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)


Most of the approaches for understanding user preferences or taste are based on having explicit feedback from users. However, in many real-life situations we need to rely on implicit feedback. To analyze the relation between implicit and explicit feedback, we conduct a user experiment in the music domain. We find that there is a strong relation between implicit feedback and ratings. We analyze the effect of context variables on the ratings and find that recentness of interaction has a significant effect. We also analyze several user variables. Finally, we propose a simple linear model that relates these variables to the rating we can expect to an item. Such mapping would allow to easily adapt any existing approach that uses explicit feedback to the implicit case and combine both kinds of feedback.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Oard, D.W., Kim, J.: Implicit feedback for recommender systems. In: AAAI Workshop on Recommender Systems (1998)Google Scholar
  3. 3.
    Potter, G.: Putting the collaborator back into collaborative filtering. In: 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition (2008)Google Scholar
  4. 4.
    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
  5. 5.
    Amatriain, X., Pujol, J.M., Oliver, N.: I like it... I like it not: Evaluating user ratings noise in recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 247–258. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Jawaheer, G., Szomszor, M., Kostkova, P.: Characterisation of explicit feedback in an online music recommendation service. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 317–320 (2010)Google Scholar
  7. 7.
    L., Baltrunas, X.A.: Towards time-dependant recommendation based on implicit feedback. In: Workshop on Context-Aware Recommender Systems (CARS 2009) in ACM Recsys 2009 (2009)Google Scholar
  8. 8.
    Celma, O.: Music recommendation and discovery in the long tail (2008)Google Scholar
  9. 9.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of ICDM 2008 (2008)Google Scholar
  10. 10.
    Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: SIGIR 1994: Proceedings of the 17th Annual International ACM SIGIR Conference, pp. 272–281. Springer-Verlag New York, Inc., Heidelberg (1994)Google Scholar
  11. 11.
    Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  12. 12.
    Oard, D., Kim, J.: Modeling information content using observable behavior. In: Proc. of the ASIST Annual Meeting, pp. 481–488 (2001)Google Scholar
  13. 13.
    Koh, N.S., Hu, N., Clemons, E.K.: Do online reviews reflect a product’s true perceived quality? - an investigation of online movie reviews across cultures. Electronic Commerce Research and Applications (2010)Google Scholar
  14. 14.
    Jawaheer, G., Szomszor, M., Kostkova, P.: Comparison of implicit and explicit feedback from an online music recommendation service. In: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems (2010)Google Scholar
  15. 15.
    Kordumova, S., Kostadinovska, I., Barbieri, M., Pronk, V., Korst, J.: Personalized implicit learning in a music recommender system. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 351–362. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Kutner, M., Nachtschiem, C., Wasserman, W., Neter, J.: Applied Linear Statistical Models, 4th edn. McGraw-Hill, New York (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Denis Parra
    • 1
  • Xavier Amatriain
    • 2
  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUSA
  2. 2.Telefonica Research, Diagonal 00BarcelonaSpain

Personalised recommendations