The sites new reading recommendations are generated using a set of propriety algorithms which look at over 20 billion different data points. Perhaps most importantly, it takes into account the stated preferences of of its nearly 6 million users, for whom rating books is already a key component of using the site.
“With Goodreads, it’s as if you combine your favorite librarian, your best friend, and a database of two million book titles into one person and ask ‘what should I read next?'” said Chandler. “We’re the Netflix of book recommendations. As members add more reviews and ratings, we keep improving our suggestions for them.”
When most people hear “the Netflix of book recommendations” they tend to think of another Internet giant known for its powerful recommendation engine: Amazon. Goodreads says it can provide better book recommendations than Amazon can because it has more data about what people actually like and dislike, as opposed to just purchases, browsing history and ratings.
“For example, we have more than 174,000 ratings of the best-selling ‘The Help’ while Amazon only has around 4,400,” said Chandler.
The site’s book recommendations are heavily influenced by each user’s book rating history, so people are encouraged to rate 20 books before checking out their suggested reading list. The service is now available in beta to all Goodreads users.
Yet another crowdsourcing case. Nice to know they improve beyond Amazon, with more data points integrated. Also nice to make use of rating history.