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Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs
This paper summarises objectives, organisation, and results of the first
news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted
the evaluation of news recommendation algorithms in the form of a campaignstyle
evaluation lab. Participants had the chance to apply two types of evaluation
schemes. On the one hand, participants could apply their algorithms onto a data
set. We refer to this setting as off-line evaluation. On the other hand, participants
could deploy their algorithms on a server to interactively receive recommendation
requests. We refer to this setting as on-line evaluation. This setting ought to reveal
the actual performance of recommendation methods. The competition strived to
illustrate differences between evaluation with historical data and actual users. The
on-line evaluation does reflect all requirements which active recommender systems
face in practise. These requirements include real-time responses and large-scale
data volumes. We present the competition’s results and discuss commonalities
regarding participants’ approaches