Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs

AutorBenjamin Kille, Torben Brodt, Tobias Heintz, Frank Hopfgartner, Andreas Lommatzsch, Jonas Seiler
QuelleCLEF 2014 Evaluation Labs and Workshop 
LinksBibTeX   |   Uni-Bibliothek 

This paper summarises objectives,organisation,andresultsofthefirst news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted the evaluation of news recommendation algorithms in the form of a campaign-style 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.