Classifying and suggesting documents that point to our goals. An old problem, but now being done with new tools. 'Geometric Data Processing' an interesting term, see more on that here. "Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines"
Finding a good read among billions of choices
As natural language processing techniques improve, suggestions are getting speedier and more relevant.
Kim Martineau | MIT Quest for Intelligence
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. As natural language processing techniques improve, those “you might also like” suggestions are getting speedier and more relevant. .... "
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