Finding a good read among billions of choices

With billions of books, news stories, and documents online, there's never been a better time to be reading -- if you have time to sift through all the options. "There's a ton of text on the internet," says Justin Solomon, an assistant professor at MIT. "Anything to help cut through all that material is extremely useful." With the MIT-IBM Watson AI Lab and his Geometric Data Processing Group at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the Conference on Neural Information Processing Systems (NeurIPS). Their method combines three popular text-analysis tools -- topic modeling, word embeddings, and optimal transport -- to deliver better, faster results than competing methods on a popular benchmark for classifying documents. If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar.