[MUSIC] So let's start with describing this
task in a little bit more detail. Okay, so let's say we're sitting here,
reading an article. Here we are, reading our article. And actually it's Carlos
who's reading this article, because this is an article about soccer. Or as he would call it football if
he was wearing his Argentina jersey or footsie ball if he was
wearing his Brazil jersey and clearly, I'm not pronouncing
either of those words correctly. Carlos can correct me later. But the point is that he likes
this article and what we'd like to do is retrieve another article that
he might be interested in reading. But a question is how do we do this? There are lots and lots of articles out
there and I can't expect him to go and read each of them and say yes,
he's interested or no, he's not. So we like to think of a way to
automatically retrieve a document that might be of interest to him. By questions here are first, how to we
measure similarity between articles? We need to have that in order to say that
this article is similar to the one he's reading now and
might also be of interest to him. Or that, here's a large set of
articles that are very different and probably are not of interest to him. And then the second question is, how
are we gonna search over the articles that exist out there and
retrieve the next article to recommend. [MUSIC]