Abstract

The CLEF NewsREEL challenge allows researchers to evaluate news recommendation algorithms both online (NewsREEL Live) and offline (News- REEL Replay). Compared with the previous year NewsREEL challenged participants with a higher volume of messages and new news portals. In the 2017 edition of the CLEF NewsREEL challenge a wide variety of new approaches have been implemented ranging from the use of existing machine learning frameworks, to ensemble methods to the use of deep neural networks. This paper gives an overview over the implemented approaches and discusses the evaluation results. In addition, the main results of Living Lab and the Replay task are explained.

@inproceedings{KilleEtAl-NewsREEL-ExtendedLabOverview2017,
author = {Benjamin Kille and Andreas Lommatzsch and Frank Hopfgartner and Martha Larson and Torben Brodt},
title = {{CLEF NewsREEL 2017}},
booktitle = {Working Notes of CLEF 2017},
year = {2017},
isbn = {1613-0073},
volume={1866},
numpages = {13},
location = {Dublin, Ireland},
publisher = {CEUR Workshop Proceedings},
note = {http://ceur-ws.org/Vol-1866/invited_paper_17.pdf}
}