Overview of NewsREEL’16: Multi-dimensional Evaluation of Real-Time Stream-Recommendation Algorithms

  • Benjamin Kille
  • Andreas Lommatzsch
  • Gebrekirstos G. Gebremeskel
  • Frank Hopfgartner
  • Martha Larson
  • Jonas Seiler
  • Davide Malagoli
  • András Serény
  • Torben Brodt
  • Arjen P. de Vries
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9822)

Abstract

Successful news recommendation requires facing the challenges of dynamic item sets, contextual item relevance, and of fulfilling non-functional requirements, such as response time. The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to tackle news recommendation and to optimize and evaluate their recommender algorithms both online and offline. In this paper, we summarize the objectives and challenges of NewsREEL 2016. We cover two contrasting perspectives on the challenge: that of the operator (the business providing recommendations) and that of the challenge participant (the researchers developing recommender algorithms). In the intersection of these perspectives, new insights can be gained on how to effectively evaluate real-time stream recommendation algorithms.

Keywords

Recommender Systems News Multi-dimensional Evaluation Living Lab Stream-based Recommender 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Benjamin Kille
    • 1
  • Andreas Lommatzsch
    • 1
  • Gebrekirstos G. Gebremeskel
    • 2
  • Frank Hopfgartner
    • 3
  • Martha Larson
    • 4
    • 7
  • Jonas Seiler
    • 5
  • Davide Malagoli
    • 6
  • András Serény
    • 8
  • Torben Brodt
    • 5
  • Arjen P. de Vries
    • 7
  1. 1.TU BerlinBerlinGermany
  2. 2.CWIAmsterdamThe Netherlands
  3. 3.University of GlasgowGlasgowUK
  4. 4.TU DelftDelftThe Netherlands
  5. 5.Plista GmbHBerlinGermany
  6. 6.ContentWise R&D — MoviriMilanItaly
  7. 7.Radboud University NijmegenNijmegenThe Netherlands
  8. 8.Gravity ResearchBudapestHungary

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