I’m always interested in new ways that people are using data to gain insight into readerly behavior. So here’s a neat idea: BookRx is a recently launched experiment from the Knight Lab, which crawls a Twitter feed (assuming one tweets about books), to make book recommendations.
The Huffington Post has a good interview, with some additional information —
“How does it work?
BookRx works in two phases. In the first phase, it analyzes your tweets (in terms of the words, Twitter usernames, and hashtags you use) and compares them to terms that are correlated with book categories. In the second phase, it looks within those categories to find specific books to recommend, again based on correlations with the terms in your tweets. The first phase is very fast but the second takes a few seconds.
What can people’s Twitter word usage tell us about their personalities?
That’s a really interesting question. We’re really interested in how Twitter can hold up a mirror to ourselves, and seeing BookRx’s recommendations might be one way to do that. That’s one of the reasons we show you the terms you used that made the system think you might be interested in a book it’s recommending to you — to make its operation a bit more visible.”
The Secret Sauce of book recommendations has always been rather mysterious, so I do like the BookRx approach. From Mashable, “New Web App Recommends Books Based on Your Tweets” —
“… For some, there is something innately unsettling about AI predictions. It is even more disturbing when the computer is accurate. Unlike sites like Amazon and Google, however, BookRx shows you the exact words you tweeted that led to its various recommendations.”
I think the idea for the experiment is very clever. It’s something to keep an eye on, particularly if they have good luck growing their user base — if BookRx is something that catches on, it’ll be interesting to see what kinds of information it can tell us about reading recommendations (and how good, or bad, those recommendations are).