[visionlist] Call-for-Participation - Data Released: MediaEval 2018 Recommending Movies using Content: Which Content is Key? Task

Bogdan Ionescu bogdanlapi at gmail.com
Sat Jul 28 09:31:34 -05 2018

[Apologies for cross-postings]

Recommending Movies using Content: Which Content is Key? Task
2018 MediaEval Benchmarking Initiative for Multimedia Evaluation
Website: http://www.multimediaeval.org/mediaeval2018/content4recsys/
Register here: https://docs.google.com/forms/d/e/1FAIpQLSfw11pDSAJb92K6lLH0DU3r85NMOj1Ww2A5R01iqQE985fqdg/viewform

The task addresses the question of which kinds of content are most
helpful for predicting the reception that a movie will receive by its
audience, as reflected in its ratings. There are two aspects to this
question: (1) which part of the movie or trailer are most important
(e.g., type of scene, beginning middle end) and (2) which aspects of
the content are important (e.g., what is depicted, how it is edited).
Because trailers and movie clips are different, we expect that it will
be most productive to take their differences into account in this
task. For example, movie clips are made usually with a few long shots
focusing on a particular scene, while trailers use many short-length
shots summarizing the entire movie.

An important challenge of the task is addressing the fact that user
ratings on movies are atomic (i.e., users assign them to the movie as
a whole), and it is not clear in how far we can assume that different
parts of the movie or trailer contribute compositionally to the
rating. This task explores the idea that it is productive to look for
short segments that are predictive of the rating, and that it is not
necessary to process the full-length movie for successful rating
prediction. The advantages of a system that uses short segments are
twofold: first of all, short segments allow for a dramatic reduction
in computational time, and, second, short segments are more readily
available than full movies.

The overall goal of the task is to use content-based features to
predict how a movie is received by its viewers. Task participants must
create an automatic system that can predict the average ratings that
users assign to movies (representing the global appreciation of the
movie by the audience) and also the rating variance (representing the
agreement/disagreements between user ratings). The input to the system
is a set of audio, visual and text features derived from trailers and
selected movie scenes (movie clips).

Target communities
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.

Participants are supplied with audio, visual and text features
computed from trailers and clips corresponding to about 800 unique
movies in the well-known MovieLens 20M dataset. This allows to make
use of the user ratings and tags (keywords). Each movie is accompanied
by a set of links (mainly on YouTube) to different samples of movie
clips, each focusing on a particular scene and semantic.

Participants to the task are invited to present their results during
the annual MediaEval Workshop, which will be held 29-31 October 2018
at EURECOM, Sophia Antipolis, France. Working notes proceedings are to
appear with CEUR Workshop Proceedings (ceur-ws.org).

Important dates (tentative)
Development data release: 20 July
Test data release: 15 August (tentative)
Runs due: 25 September
Working notes papers due: 17 October
MediaEval Workshop, Sophia Antipolis, France: 29-31 October

Task coordination
Yashar Deldjoo, Politecnico di Milano, Italy
Thanasis Dritsas, TU Delft, Netherlands
Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
Anuva Agarwal, Carnegie Mellon University, USA
Bogdan Ionescu, University Politehnica of Bucharest, Romania
Markus Schedl, Johannes Kepler University Linz, Austria

On behalf of the organizers,

Prof. Bogdan IONESCU
ETTI - University Politehnica of Bucharest

Research Center CAMPUS

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