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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)

Abstract

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.

Keywords

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.

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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

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