In 2000, 60,234 titles between movies and TV shows were released, according to the IMDB source. In 2010, 165,830 titles and in 2016, 190,275 titles were filmed. We can only notice that the movie release industry is in perpetual increase and the databases aggregating the data are in need of more information to expand.
The idea behind this challenge is to facilitate the genre labeling of movies from their summaries and thus to help with categorization of the movies database.
The tagging of movies' genres is still a manual process which involves the collection of users' suggestions. Automatic genres classification of a movie based on its summary not only speeds up the classification process by providing a list of suggestion but the result may potentially be more accurate than an untrained human.
This challenge was generated using chalab.
The submission will be evaluated using the Weighted F1-Score.
It is the scoring method given by scikit-learn with a weighted average and used in multi-label tasks.
The evaluation metric for this competition is Weighted F1-Score used in multi-label learning literature. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (TP) to all predicted positives (TP + FP). Recall is the ratio of true positives to all actual positives (TP + FN). The F1 score is given by:
The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
For the submission, we provide a starting kit, in the "participate" (Get Data) section, with a submission example. You can modify it, recompute the results, compress it back to .zip in order to submit the compressed file into the "participate' section.
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Start: Nov. 25, 2016, 10:33 a.m.
Description: Development phase: create models and submit them or directly submit results on validation and/or test data; feed-back are provided on the validation set only.
Start: April 29, 2017, midnight
Description: Final phase: submissions from the previous phase are automatically cloned and used to compute the final score. The results on the test set will be revealed when the organizers make them available.
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