In this paper we introduce the MediaEval 2018 task Recommending Movies Using Content. It focuses on predicting overall scores that users give to movies, i.e., average rating (representing overall appreciation of the movies by the viewers) and the rating variance/standard deviation (representing agreement/disagreement between users) using audio, visual and textual features derived from selected movie scenes. We release a dataset of movie clips consisting of 7K clips for 800 unique movies. In the paper, we present the challenge, the dataset and ground truth creation, the evaluation protocol and the requested runs. Copyright held by the owner/author(s).
The MediaEval 2018 movie recommendation task: Recommending movies using content / Deldjoo, Yashar; Gabriel Constantin, Mihai; Dritsas, Athanasios; Ionescu, Bogdan; Schedl, Markus. - ELETTRONICO. - 2283:(2018). (Intervento presentato al convegno Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018 tenutosi a Sophia Antipolis, France nel October 29-31, 2018).
The MediaEval 2018 movie recommendation task: Recommending movies using content
Yashar Deldjoo
;
2018-01-01
Abstract
In this paper we introduce the MediaEval 2018 task Recommending Movies Using Content. It focuses on predicting overall scores that users give to movies, i.e., average rating (representing overall appreciation of the movies by the viewers) and the rating variance/standard deviation (representing agreement/disagreement between users) using audio, visual and textual features derived from selected movie scenes. We release a dataset of movie clips consisting of 7K clips for 800 unique movies. In the paper, we present the challenge, the dataset and ground truth creation, the evaluation protocol and the requested runs. Copyright held by the owner/author(s).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
