“In a recommender system, the hidden information represents users’ unknown interests and the observed data consist of products they have purchased thus far. If a user recently bought a bike, she is interested in biking/outdoors, and is more likely to buy biking accessories in the near future. We can model her interest as a hidden variable and infer it from her buying pattern. To discover such relationships, however, we need to observe a whole lot of buying patterns from lots of users—making this problem a big data one. (Anima Anandkumar)”