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Benchmarking News Recommendations: The CLEF NewsREEL Use Case
TLDR
The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms in real-time. Expand
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Stream-Based Recommendations: Online and Offline Evaluation as a Service
TLDR
In this paper, we discuss the objectives and challenges of the NewsREEL lab. Expand
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Overview of CLEF NewsREEL 2015: News Recommendation Evaluation Lab
TLDR
This paper summarizes the settings and results of CLEF NewsREEL 2015. Expand
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Overview of NewsREEL'16: Multi-dimensional Evaluation of Real-Time Stream-Recommendation Algorithms
TLDR
In this paper, we discuss how evaluation of real-time stream recommendation algorithms presents challenges that cannot be so easily controlled for. Expand
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Idomaar: A Framework for Multi-dimensional Benchmarking of Recommender Algorithms
TLDR
We present the Idomaar framework, which enables the efficient, reproducible evaluation of recommender algorithms in real-world stream-based scenarios. Expand
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CLEF NewsREEL 2016: Comparing Multi-dimensional Offline and Online Evaluation of News Recommender Systems
TLDR
This paper provides an overview of the NewsREEL scenario, outlines this year’s campaign, presents results of both tasks, and discusses the approaches of participating teams. Expand
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