mediaeval 2015 - introduction to a task on context of experience: recommending videos suiting a...
TRANSCRIPT
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Context of ExperienceMichael Riegler, Martha Larson, Concetto Spampinato, Jonas
Markussen, Pål Halvorsen, Carsten Griwodz, Konstantin Pogorelov
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We had an idea about intentbut…
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Make a task for Mediaeval…
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Wait… What is this Mediaeval?
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What most of you might think…
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What I thought…
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What my girlfriend thinks (and Google apparently)…
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Media Eval = Benchmark
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Make a task…lets…
add -> planes give -> situation
context -> recommender systems
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“The world as we have created it is a process of our thinking. It cannot be changed without changing
our thinking.”–Albert Einstein
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Context of ExperienceRecommending Videos Suiting a Watching Situation
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Goals of the taskExplore 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
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Runs (tasks)
Use all information available
Content (visual, audio…)
Metadata (ratings, comments…)
More…
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DatasetMovies collected from KLM over 3 month
Crowdsourced user opinions
Only Workers that have experience
Ranking of videos to get consent
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Dataset detailsAround 300 movies
Split into test and train set (30/70)
Metadata (names, ratings…)
Audio features
Visual features
Links to videos
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First experimentsUsed 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
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First experimentsUsed 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
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First experimentsUsed 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
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First experimentsUsed 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
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First experimentsUsed 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
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Interesting insightsNo correlation between popular ranking sites (IMDB, Metacritic…) and what people like
Some genres are favourite (Comedies, Family movies leading)
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Current state
First year of the task
Many people are interested
Submitted dataset paper to MMSys
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Planed ImprovementsGet 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
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Thank you!And if you are interested we are always looking for help ;-)