Six finalists in the book discovery Publishing Hackathon; winner to be announced at BEA

The Publishing Hackathon, held this past weekend at coworking space The Alley in New York, gave 30 teams a little over a day to come up with an idea for a book discovery startup, build a demo and pitch it to a panel of judges. Six finalists were chosen Sunday by a panel of judges including Perseus CMO Rick Joyce and NYC Seed managing director Owen Thomas.

evokeThe winning startup will be announced at Book Expo America on May 31 and will receive $10,000 and the chance to pitch its product to William Morris Endeavor co-CEO Ari Emanuel.

Here are the finalists:

  • BookCity: A way to find books set in your travel destination [photo illustrating this post]
  • Captiv: Makes book recommendations based on your Twitter activity
  • Coverlist: A solution that focuses on browsing book jackets
  • Evoke:  A way to discover young adult literature through characters and browse books by emotion: “Readers may determine if they wish to be inspired, challenged, amused, or informed during their next read based on content generated by an audience-in-common” [photo on right]
  • KooBrowser: Makes book recommendations based on your web browsing habits [photo on left]
  • LibraryAtlas: A book discovery solution based on geolocation

KooBrowserI attended the demos on Sunday. A few thoughts:

  • It’s hard to come up with a book discovery idea that is not similar to Goodreads in some way, though the finalists above did a good job. Many of the teams built ideas on Goodreads data or pulled other information from it.
  • Book-recommendation algorithms were big (and, you’ll notice, didn’t make the cut above). A lot of teams described their idea as “Pandora for books” or “Netflix for books,” but they just meant that the software serves content recommendations, not that it actually streams content.
  • Of the above, I thought that KooBrowser seemed most useful and like something I’d actually use. The idea is that, if you’re reading an article online, you could pull up a list of book recommendations based on the content of that article. The success of KooBrowser depends on how good the recommendations are, of course. But this idea seems to fit well into users’ actual everyday activity without being annoying. (I’m still not sold on the idea of receiving book recommendations pushed to my phone when I’m out and about.)