Abstract

News recommender systems provide users with access to news stories that they find interesting and relevant. As other online, stream-based recommender systems, they face particular challenges, including limited information on users’ preferences and also rapidly fluctuating item collections. In addition, technical aspects, such as response time and scalability, must be considered. Both algorithmic and technical considerations shape working requirements for real-world recommender systems in businesses. NewsREEL represents a unique opportunity for students to experience realistic conditions and to enlarge their skill sets. The NewsREEL Challenge requires participants to conduct data-driven experiments in NewsREEL Replay as well as deploy their best models into NewsREEL Live’s living lab. This paper presents NewsREEL 2017 and also provides insights into the effectiveness of NewsREEL to support the goals of instructors teaching recommender systems to students. We discuss the experiences of NewsREEL participants as well as those of instructors teaching recommender systems to students, and in this way, we showcase NewsREEL’s ability to support the education of future data scientists.

@inproceedings{LommatzschEtAl:Stream-basedRecommenderTaskForEvaluationAndEducation,
author = {Andreas Lommatzsch and Benjamin Kille and Frank Hopfgartner and Martha Larson and "Ozlem "Ozg"obek, Torben Brodt and Jonas Seiler},
title = {CLEF 2017 NewsREEL Overview: A Stream-based Recommender Task for Evaluation and Education},
booktitle = {{CLEF}'17: Proceedings of the 8th International Conference of the {CLEF} Initiative},
publisher = {Springer International Publishing},
year = {2017},
series = {LNCS, vol. 10456},
location = {Dublin, Ireland},
numpages = {16},
isbn = {978-3-319-65813-1},
}