In 2008 it introduced a "best match" algorithm that displays the most relevant listings to what people have typed into the search box, even when the terms don't obviously match.

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The tool will collect data and start populating a shadow Twitter account with a daily tweet that the algorithm determines match the person's habits and interests.

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While still at McKinsey, Artun wrote a machine learning algorithm that clustered customers who tended to buy items similar to each other and related that cluster to various sales coverage efforts, which were the items that salespeople seemed to be most comfortable selling together, and built a matching algorithm to match salespeople with customers.

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Finally, an algorithm was created to match the sound of the headphones to the sound of the studio as closely as possible.

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The researchers' algorithm tries to detect the theme or topic of each story, and match it to other similar stories.

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By creating a mathematical algorithm, the charity hopes to predict whether someone could be a good match using the information gathering in initial testing.

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