It's NCAA tournament time here in the U.S. and plenty of bracketologists are turning to Nate Silver for his statistical expertise. Silver, of course, is known for his book, The Signal and the Noise, as well as predicting presidential elections and Major League Baseball player performance. I'm not aware of any statistical analysis he's done in the book recommendation space but I know someone who has applied Silver's thinking to help us figure out what book we should read next.
I'm talking about Stephanie Sun and a terrific article she wrote called Nate Silverizing Book Recommendations. I encourage you to read the entire piece, even if it's been awhile since your last statistics class.
As you read Stephanie's article, think about how book recommendation engines are likely to get better and better down the road. As she also points out, it's not just about helping consumers discover their next great read. This same analysis can also be used to help editors prioritize their time when faced with a stack of manuscripts to review.
Many will cringe when told that this sort of curation and serendipity can be reduced to an algorithm. That's not what anyone is suggesting though; the algorithm can simply be one of many tools to help improve discovery. And although it will never be perfect, look at how search engines have evolved since the early days of the web. Today we're often limited to the very simplistic "people who bought X also bought Y" type of recommendation. We're still in the early days of solving the discovery and recommendation problem in our industry and we need smart people like Stephanie Sun to drive improvement in our search and recommendation results.