CLEF 2017 NewsREEL Overview: A Stream-based Recommender Task for Evaluation and Education

Lommatzsch, A. , Kille, B., Hopfgartner, F. , Larson, M., Brodt, T., Seiler, J. and Özgöbek, Ö. (2017) CLEF 2017 NewsREEL Overview: A Stream-based Recommender Task for Evaluation and Education. In: 8th International Conference of the CLEF Association: Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2017), Dublin, Ireland, 11-14 Sept 2017, pp. 239-254. ISBN 9783319658124 (doi: 10.1007/978-3-319-65813-1_23)

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.

• Item Type: Conference Proceedings
• Additional Information: Published in volume 10439 of Lecture Notes in Computer Science.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Hopfgartner, Dr Frank
• Authors: Lommatzsch, A., Kille, B., Hopfgartner, F., Larson, M., Brodt, T., Seiler, J., and Özgöbek, Ö.
• College/School: College of Arts & Humanities > School of Humanities > Information Studies
• Journal Name: Lecture Notes in Computer Science
• Publisher: Springer
• ISSN: 0302-9743
• ISBN: 9783319658124
• Copyright Holders: Copyright © 2017 Springer International Publishing AG
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Related URLs

• Organisation