Overview of newsreel’16
Multi-dimensional evaluation of real-time stream-recommendation algorithms
Benjamin Kille (Technical University of Berlin)
Andreas Lommatzsch (Technical University of Berlin)
Gebrekirstos G Gebremeskel (Centrum Wiskunde & Informatica (CWI))
Frank Hopfgartner (University of Glasgow)
Martha Larson (Radboud Universiteit Nijmegen, TU Delft - Multimedia Computing)
Jonas Seiler (Plista GmbH)
Davide Malagoli (ContentWise R&D)
András Serény (Gravity Research)
Torben Brodt (Plista GmbH)
Arjen P De Vries (Radboud Universiteit Nijmegen)
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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.