The MediaEval 2018 Movie Recommendation Task:

Yashar Deldjoo; Mihai Gabriel Constantin; Athanasios Dritsas; B. Ionescu; Markus Schedl

The MediaEval 2018 Movie Recommendation Task:

Yashar Deldjoo, Mihai Gabriel Constantin, Athanasios Dritsas, B. Ionescu, Markus Schedl

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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.

Original language	English
Title of host publication	Working Notes Proceedings of MediaEval 2018: Multimedia Benchmark Workshop
Number of pages	3
Publication status	Published - Nov 2018

Fields of science

202002 Audiovisual media
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

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title = "The MediaEval 2018 Movie Recommendation Task:",

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.",

author = "Yashar Deldjoo and Constantin, \{Mihai Gabriel\} and Athanasios Dritsas and B. Ionescu and Markus Schedl",

year = "2018",

month = nov,

language = "English",

booktitle = "Working Notes Proceedings of MediaEval 2018: Multimedia Benchmark Workshop",

}

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T1 - The MediaEval 2018 Movie Recommendation Task:

AU - Deldjoo, Yashar

AU - Constantin, Mihai Gabriel

AU - Dritsas, Athanasios

AU - Ionescu, B.

AU - Schedl, Markus

PY - 2018/11

Y1 - 2018/11

N2 - 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.

AB - 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.

M3 - Conference proceedings

BT - Working Notes Proceedings of MediaEval 2018: Multimedia Benchmark Workshop

ER -