Abstract

News reader struggle as they face ever increasing numbers of articles. Digital news portals are becoming more and more popular. They route news items to visitors as soon as they are published. The rapid rate at which new news is published gives rise to a selection problem, since the capacity of new portal videos to absorb news is limited. To address this problem, new portals deploy news recommender systems in order to support their visitors in selecting items to read. This paper summarizes the settings and results of CLEF NewsREEL 2015. The lab challenged participants to compete in either a "living lab" (Task 1) or an evaluation that replayed recorded streams (Task 2). The goal was to create an algorithm that was able to generate news items that users would click, respecting a strict time constraint.

@inproceedings{KilleEtAl:OverviewOfClefNewsReel2015,
author = {Benjamin Kille and Andreas Lommatzsch and Roberto Turrin and Andras Sereny and Martha Larson and Torben Brodt and Jonas Seiler and Frank Hopfgartner},
title = {{NewsREEL} 2015: Summary of the News Recommendation Evaluation Lab},
booktitle = {Working Notes for the CLEF 2015 Conference},
year = {2015},
location = {Toulouse, France},
numpages = {13},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-1391/inv-pap4-CR.pdf},
note ={Vol-1391, urn:nbn:de:0074-1391-8}
}