Monitoring Recommender Systems: A Business Intelligence Approach

  • Catarina Félix
  • Carlos Soares
  • Alípio Jorge
  • João Vinagre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8584)


Recommender systems (RS) are increasingly adopted by e-business, social networks and many other user-centric websites. Based on the user’s previous choices or interests, a RS suggests new items in which the user might be interested. With constant changes in user behavior, the quality of a RS may decrease over time. Therefore, we need to monitor the performance of the RS, giving timely information to management, who can than manage the RS to maximize results. Our work consists in creating a monitoring platform - based on Business Intelligence (BI) and On-line Analytical Processing (OLAP) tools - that provides information about the recommender system, in order to assess its quality, the impact it has on users and their adherence to the recommendations. We present a case study with Palco Principal, a social network for music.


Recommender System Data Warehouse Collaborative Filter Business Intelligence Negative Action 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Billsus, D., Pazzani, M.: User modeling for adaptive news access. User Modeling and User-Adapted Interaction 10(2-3), 147–180 (2000)CrossRefGoogle Scholar
  2. 2.
    Koychev, I., Schwab, I.: Adaptation to drifting user’s interests. In: Proceedings of ECML2000 Workshop: Machine Learning in New Information Age, pp. 39–46 (2000)Google Scholar
  3. 3.
    Kimball, R.: The data warehouse toolkit: The Complete Guide to Dimensional Modeling. Wiley (2006)Google Scholar
  4. 4.
    Pentaho: Pentaho website, (accessed: January 17, 2014)
  5. 5.
    Pentaho: Pentaho community website, (accessed: January 17, 2014)
  6. 6.
    Saiku: Saiku website, (accessed: January 17, 2014)
  7. 7.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  8. 8.
    Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce. EC 1999, pp. 158–166. ACM, New York (1999)Google Scholar
  9. 9.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. WWW 2001, pp. 285–295. ACM, New York (2001)Google Scholar
  10. 10.
    Nowak, M., Nass, C.: Effects of behavior monitoring and perceived system benefit in online recommender systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI 2012, pp. 2243–2246. ACM, New York (2012)Google Scholar
  11. 11.
    Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems. RecSys 2011, pp. 157–164. ACM, New York (2011)Google Scholar
  12. 12.
    Domingues, M.A., Gouyon, F., Jorge, A.M., Leal, J.P., Vinagre, J., Lemos, L., Sordo, M.: Combining usage and content in an online recommendation system for music in the long tail. IJMIR 2(1), 3–13 (2013)Google Scholar
  13. 13.
    Kohavi, R., Longbotham, R., Walker, T.: Online experiments: Practical lessons. IEEE Computer 43(9), 82–85 (2010)CrossRefGoogle Scholar
  14. 14.
    Brazdil, P., Giraud-carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. In: Cognitive Technologies. Springer (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Catarina Félix
    • 1
    • 2
  • Carlos Soares
    • 1
    • 3
  • Alípio Jorge
    • 2
    • 4
  • João Vinagre
    • 2
    • 4
  1. 1.INESC TECPortugal
  2. 2.Faculdade de Ciências da Universidade do PortoPortugal
  3. 3.Faculdade de Engenharia da Universidade do PortoPortugal
  4. 4.LIAAD-INESC TECPortugal

Personalised recommendations