Lommatzsch, A., Kille, B., Hopfgartner, F. orcid.org/0000-0003-0380-6088 et al. (4 more authors) (2017) CLEF 2017 NewsREEL overview: A stream-based recommender task for evaluation and education. In: Jones, G., Lawless, S., Gonzalo, J., Kelly, L., Goeuriot, L., Mandl, T., Cappellato, L. and Ferro, N., (eds.) Experimental IR Meets Multilinguality, Multimodality, and Interaction. 8th International Conference of the CLEF Association, 11-14 Sep 2017, Dublin, Ireland. Lecture Notes in Computer Science, 10456 . Springer , pp. 239-254. ISBN 978-3-319-65812-4
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 to evaluate recommendation algorithms and 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a paper subsequently published in Jones G. et al. (eds) Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2017. Lecture Notes in Computer Science, vol 10456. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Recommender systems; News; Evaluation; Living lab; Stream-based recommender |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Mar 2019 15:41 |
Last Modified: | 18 Mar 2019 15:41 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-65813-1_23 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140378 |