Abstract

The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms. The goal is to create an algorithm that is able to generate news items that users would click, respecting a strict time constraint. The lab challenges participants to compete in either a "living lab" (Task 1) or perform an evaluation that replays recorded streams (Task 2). In this report, we discuss the objectives and challenges of the NewsREEL lab, summarize last year's campaign and outline the main research challenges that can be addressed by participating in NewsREEL 2016.

@article{Hopfgartner:2016:BNR:2888422.2888443,
 author = {Hopfgartner, Frank and Brodt, Torben and Seiler, Jonas and Kille, Benjamin and Lommatzsch, Andreas and Larson, Martha and Turrin, Roberto and Ser{'e}ny, Andr'{a}s},
 title = {Benchmarking News Recommendations: The CLEF NewsREEL Use Case},
 journal = {SIGIR Forum},
 issue_date = {December 2015},
 volume = {49},
 number = {2},
 month = jan,
 year = {2016},
 issn = {0163-5840},
 pages = {129--136},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/2888422.2888443},
 doi = {10.1145/2888422.2888443},
 acmid = {2888443},
 publisher = {ACM},
 address = {New York, NY, USA},
}