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Machine Learning Selects World's Next Top Models

By iTnews Australia

August 24, 2015

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Indiana University researchers have trained machine-learning algorithms to accurately predict the next batch of successful female fashion models.

Success was measured in the number of runways the new models walked in a fashion season.

The algorithms correctly predicted six out of eight fashion models who became popular during the season, using training data from the past season only. The algorithms also successfully identified six out of seven fashion models who did not perform in any top event, according to the researchers.

The past season data was collected from several fashion databases that track the identity and attributes of the models, their agency representation, and in which shows they appear. The researchers also examined which of the "new faces" from the fashion databases had Instagram accounts and tracked their activity for any correlation between social media presence and runway success.

The researchers found Instagram data does have an effect on new models' success, but it does not tell the whole story. "As the impact of social media--especially Instagram--becomes significant in the fashion industry, predictive methods have the potential to leverage collective attention and the wisdom of the broader user population, which reflect some of the popularity of fashion models, to predict their career success," the researchers note.

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