The Lending Club is an online marketplace for loans. As a borrower, you can apply for a loan, and if accepted, your loan gets listed in the marketplace. As an investor, you can browse loans in the marketplace, and invest in individual loans at your discretion. This peer to peer model has many advantages over traditional banking counterparts, for example, lower overhead costs, lower cost of capital, etc.

But what excites me the most about peer to peer lending is the democratization of data. As an investor, you can see each and every rejected, completed, ongoing, and available loan. While loan data excludes personally identifiable information, it does include attributes like credit rating, location, college education level, lines of credit, and descriptions of why the applicant needs the loan.

Precision and recall are two fundamental quality measures in search and information retrieval applications.  Google is fundamentally a search application. But Google doesn't need to optimize for recall, it just optimizes for precision.  When I Google for “Michael Jordan”, I’m really just looking for a single page about the basketball legend. A search application’s ability to find *all* pages about “Michael Jordan” is a measure of recall, and users don't want to read thousands of pages about Mike, so Google doesn't optimize for this. Optimizing for recall is hard, and I spent most of my academic life working on algorithms to improve this measure. At an intuitive level, these algorithms "discover" relational inferences between various entities. For example, basketball, court, and rim are related, whereas field goal, penalty shot, and slam dunk are not. At a technical level, these algorithms worked by learning a distance measure between objects. These distance metric learning algorithms did some very heavy lifting to show improvements in quality, but their improvements really only increased recall.