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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Tue, 18 Jun 2019 07:51:51 +0000
Yashar Deldjoo <[log in to unmask]>
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Yashar Deldjoo <[log in to unmask]>
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Multimedia for Recommender Systems
2019 MediaEval Benchmarking Initiative for Multimedia Evaluation
Register here:

Data release: 15 May 2019
Runs due: 20 September 2019
Results returned: 25 September 2019
Working Notes paper due: 30 September 2019
MediaEval 2019 Workshop (in France, near Nice): 27-29 October 2019

Traditionally, recommender systems and multimedia data processing are studied by separate groups of researchers. These researchers have a lot to learn from each other, and this task offers an interdisciplinary forum that promotes exactly such exchange.

An often-cited motivation exploiting features derived from multimedia is cold start. However, in this task, we also relate the importance of using multimedia in recommender systems to the drawbacks for personalization. Personalized information access comes with some caveats. Predictions become successful for some users whereas they fail for others. Understanding how multimedia affects users’ perception of items facilitates creating fair and unbiased information access systems. Recommender systems have been found to induce “filter bubbles” preventing access to some information. The high complexity of content data promises to overcome this issue as content similarities can be defined among all items. Further, the use of multimedia has potential to promote the development of recommender systems that need less user-specific interaction data in order to make recommendations, thus promoting privacy.

For this task, participants analyse items and derive feature sets combining modalities, for instance, audio, images, and text. Subsequently, they implement predictors that estimate which items will be relevant to users.

Participants can target two subtasks that cover different domains. First, the movie recommendation task asks participants to predict the average rating of movies given by users, the variance as a measure of raters’ agreement along with the popularity of movies. The provided data set includes precomputed state-of-the-art audio and visual features, and metadata. Second, the news recommendation task challenges participants to predict the reading frequencies of news articles. The provided data set comes from a set of German publishers and spans multiple months. It features text snippets, image URLs, and some pre-extracted neural image representations.

Recommender Systems (RecSys) permeate our digital landscape. Whenever users face an overwhelming amount of information, system operators introduce recommender functionalities to preselect a subset of expectedly relevant information. Multimedia RecSys investigates which role multimodal data can play to improve recommendations.

Researchers will find this task interesting if they work in the research areas of multimedia processing, personalization and recommender system, machine learning and information retrieval.

The movie dataset includes precomputed state of the art audio and visual features extracted from movie trailers, and metadata associated with each movie notably genre labels and user-generated tags. The news dataset is collected from a set of German publishers and spans multiple months. It includes text snippets, image URLs (and some pre-extracted neural image features).

Participants to the task are invited to present their results during
the annual MediaEval Workshop, which will be held by the end of
October 2019, in Nice, France, co-located with ACM Multimedia 2019.
Working notes proceedings are to appear with CEUR Workshop Proceedings

Participants with the best-performing and/or most original or creative approaches may be invited to extend their Working Note papers to full articles and submit to our Special Issue on Multimedia Recommender Systems to appear in Springer International Journal of Multimedia Information Retrieval (

Yashar Deldjoo, Polytechnic University of Bari, Italy, [log in to unmask]
Benjamin Kille, TU Berlin, Germany, [log in to unmask]
Markus Schedl, Johannes Kepler University Linz, Austria, [log in to unmask]
Andreas Lommatzsch, TU Berlin, Germany, [log in to unmask]
Jialie Shen, Queen’s University Belfast, UK, [log in to unmask]

Kind Regards,

Dr. Yashar Deldjoo
Postdoctoral Researcher, Politecnico di Bari, Italy
+39 333 277-5747<tel:+39+333+277-5747> | Skype: yashar.deldjoo<> | SisInf Lab - Information Systems Laboratory, Edoardo Orabona  St, 4 Bari, Italy Personal homepage:<>

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