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.
