The document summarizes a MediaEval task that aimed to classify videos as suitable or unsuitable for watching on an airplane based on contextual factors. Researchers collected a dataset of over 300 movies rated by crowdsourced users with airplane experience. Initial experiments classified videos using metadata, user ratings, and visual features, with metadata achieving the best F1-score of 0.583. Insights found no correlation between popular ratings sites and user choices, and that comedies and family movies were favorites. Planned improvements included collecting more data from actual air travelers and adding recommendation capabilities.

1. MediaEval Letter:
Context of Experience
Michael Riegler, Martha Larson, Concetto Spampinato,
Jonas Markussen, Pål Halvorsen, Carsten Griwodz

2. I had an Idea
but…

3. Pimp my Task…
lets add some
planes
give - situation
context - recommender
systems

4. “All meaningful and lasting change starts first in
your imagination and then works its way out.
Imagination is more important than knowledge.”
–Albert Einstein

5. Context of Experience
Recommending Videos Suiting a Watching Situation

6. Goals of the Task
Explore suitability of video
content
Watched in a certain situation
In this case airplanes
small screens, distractions…
Classify in “good” or “bad” to be
watched on an airplane

7. Video

8. Runs
Use all information
available
Content (visual, audio…)
Metadata (ratings,
comments…)
More…

9. Dataset
Movies collected from KLM
over 3 month
Crowdsourced user opinions
Only Workers that have
experience
Ranking of videos to get
consent

10. Dataset Details
Around 300 movies
Split into test and train set
(30/70)
Metadata (names, ratings…)
Audio features
Visual features
Links to videos

11. First Experiments
Used Data Precision Recall F1-score
Metadata 0,581 0,6 0,583
User ratings 0,371 0,609 0,461
Visual
information
0,447 0,476 0,458
Meta without
user ratings
0,524 0,516 0,519

12. First Experiments
Used Data Precision Recall F1-score
Metadata 0,581 0,6 0,583
User ratings 0,371 0,609 0,461
Visual
information
0,447 0,476 0,458
Meta without
user ratings
0,524 0,516 0,519

13. First Experiments
Used Data Precision Recall F1-score
Metadata 0,581 0,6 0,583
User ratings 0,371 0,609 0,461
Visual
information
0,447 0,476 0,458
Meta without
user ratings
0,524 0,516 0,519

14. First Experiments
Used Data Precision Recall F1-score
Metadata 0,581 0,6 0,583
User ratings 0,371 0,609 0,461
Visual
information
0,447 0,476 0,458
Meta without
user ratings
0,524 0,516 0,519

15. First Experiments
Used Data Precision Recall F1-score
Metadata 0,581 0,6 0,583
User ratings 0,371 0,609 0,461
Visual
information
0,447 0,476 0,458
Meta without
user ratings
0,524 0,516 0,519

16. Interesting Insights
No correlation between
popular ranking sites (IMDB,
Meteoritic…) and what
people choose
Some genres are favourite
(Comedies, Family movies
leading)

17. Planed Improvements
Get more data
Add more features
Collect data from people that
are actually flying (maybe
researchers are a good
choice)
Include a subtask for the
recommendation part

18. See You Next Year…
If you like challenges and
movies and airplanes ;-)