Right inflight? - mmsys2016.itec.aau.atContext of Experience Recommending Videos Suiting a Watching...
Transcript of Right inflight? - mmsys2016.itec.aau.atContext of Experience Recommending Videos Suiting a Watching...
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Right inflight? A dataset for exploring the automatic prediction of movies suitable for a watching situation
M. Riegler, M. Larson, C. Spampinato, P. Halvorsen, M. Lux, J. Markussen, K. Pogorelov, C. Griwodz and H. K. Stensland
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We had an idea about intent for NOSSDAV 2015
but what next?
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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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MediaEval = Benchmark
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Make an interesting
task…lets…
add -> planes give -> situation
context -> recommender systems
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We ended up with: 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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After the task…
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…we had a dataset…
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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 insightsRight not equal Right Inflight:
Aggregated user ratings (from IMDb, Metacritic, etc.) do not serve to account for people's preferences on inflight movies
Some genres are preferred, but also do not complete solve the problem
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Current usage of the dataset
Again a MediaEval task
A TU Delft lab course
Part of a ICPR 2016 proposal for a joint-challenge workshop
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Planned improvements
Get more data
Add more features
Collect data from people that are actually flying (consultants, etc.)
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Thank you!
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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