For some context, locality sensitive hashing is handy for quickly finding "similar" data points in high-dimensional space. This comes up a lot in collaborative

For some context, locality sensitive hashing is handy for quickly finding "similar" data points in high-dimensional space. This comes up a lot in collaborative filtering and recommendation problems (find similar products to hawk at your customers) and topic modeling (find similar text that is semantically getting at the same thing).