Abstract

Successful news recommendation requires facing the challenges of dynamic item sets, contextual item relevance, and of fulfilling non-functional requirements, such as response time. The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to tackle news recommendation and to optimize and evaluate their recommender algorithms both online and offline. In this paper, we summarize the objectives and challenges of NewsREEL 2016. We cover two contrasting perspectives on the challenge: that of the operator (the business providing recommendations) and that of the challenge participant (the researchers developing recommender algorithms). In the intersection of these perspectives, new insights can be gained on how to effectively evaluate real-time stream recommendation algorithms.

@inproceedings{KilleEtAl:Stream-BasedRecommendations-EvaluationAsAService,
author = {Benjamin Kille and Andreas Lommatzsch and Gebrekirstos Gebremeskel and Frank Hopfgartner and Martha Larson and Torben Brodt and Jonas Seiler and Davide Malagoli and Andras Sereny and Arjen De Vries},
title = {CLEF NewsREEL 2016: Multi-dimensional Evaluation of Real-Time Stream-Recommendation Algorithms},
booktitle = {{CLEF}'16: Proceedings of the 7th International Conference of the {CLEF} Initiative},
publisher = {Springer International Publishing},
year = {2016},
series = {LNCS, vol. 9822},
location = {Evora, Portugal},
numpages = {20},
isbn = {978-3-319-44563-2},
doi = {10.1007/978-3-319-44564-9},
note = {http://www.springer.com/de/book/9783319445632}
}