NewsREEL Multimedia at MediaEval 2018: News Recommendation with Image and Text Content

Abstract

NewsREEL Multimedia premiers 2018 as part of the MediaEval Benchmarking Initiative. The NewsREEL task combines recommen- dation algorithms with image and text analysis. Participants must predict the popularity of news items based on text snippets and annotated images. Several major German news portals have sup- plied data. The algorithms are evaluated in terms of Precision and Average Precision on unknown data. This paper describes the task and the provided data in detail and explains the applied evaluation approach.

Metadata

Authors/Creators:	Lommatzsch, A. Kille, B. Hopfgartner, F. https://orcid.org/0000-0003-0380-6088 Ramming, L.
Copyright, Publisher and Additional Information:	© 2018 The Author(s). Reproduced in accordance with the publisher's self-archiving policy.
Keywords:	Multimedia; News; Recommender Systems
Dates:	Published (online): 29 October 2018 Published: 29 October 2018
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:	05 Nov 2018 14:09
Last Modified:	13 May 2019 11:44
Published Version:	http://ceur-ws.org/Vol-2283/MediaEval_18_paper_5.p...
Status:	Published
Publisher:	CEUR-WS
Refereed:	Yes
Related URLs:	Conference

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NewsREEL Multimedia at MediaEval 2018: News Recommendation with Image and Text Content

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