MIT and IBM develop AI that recommends documents based on topic

By Kyle Wiggers | VentureBeat | December 20, 2019

Even the best text-parsing recommendation algorithms can be stymied by data sets of a certain size. In an effort to deliver faster, better classification performance than the bulk of existing methods, a team at the MIT-IBM Watson AI Lab and MIT’s Geometric Data Processing Group devised a technique that combines popular AI tools including embeddings and optimal transport. They say that their approach can scan millions of possibilities given only the historical preferences of a person, or the preferences of a group of people.

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