NewsREEL Multimedia at MediaEval’18

Author: Benjamin Kille

This year’s edition of our news recommendation challenge NewsREEL focused on multimedia data. We asked participants to estimate which articles would become popular solely based on their textual and visual features. A large-scale data set collected by our long-term partners at plista facilitated evaluating different algorithms.

The MediaEval benchmark brings different evaluation tasks together. This year’s edition took place in the time from 29 to 31 October in Nice, France. The event offers task organisers the opportunity to present their challenges. Participants can discuss ideas and illustrate their results.

Three papers have been submitted for NewsREEL Multimedia. The overview paper [1] outlines the task, details the evaluation methodology, and presents the results. The baseline paper [2] illustrates the baselines against which we compared participants’ predictions. Ciobanu et al. [3] analysed how Google’s Vision API can be used to obtain more representative labels for images.

Besides the technical programme, the MediaEval organisers had prepared social events. On the first day, we visited the Château-Musée Grimaldi and avoided drowning during the subsequent city tour. On the second day, we enjoyed dinner at Chez David. Finally, on the third day, we dined at Château Le Cagnard. During the diners, we had the opportunity to discuss and exchange ideas with a diverse group of researchers. We have come up with ideas to foster future cooperation.

Next year, MediaEval will return to Nice colocated with ACM Multimedia.


[1] Lommatzsch, A., Kille, B., Hopfgartner, F. and Ramming, L., 2018, October. NewsREEL Multimedia at MediaEval 2018: News Recommendation with Image and Text Content. In Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR-WS.


[2] Lommatzsch, A. and Kille, B., 2018. Baseline Algorithms for Predicting the Interest in News based on Multimedia Data.In Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR-WS.


[3] Ciobanu, A., Lommatzsch, A. and Kille, B., 2018. Predicting the Interest in News based On Image Annotations. In Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR-WS.


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