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The more you engage, the better the advice

Patrick Brown on the Goodreads recommendation engine and fine-tuning discoverability.

This post is part of the TOC podcast series, which we’ll be featuring here on Radar in the coming months. You can also subscribe to the free TOC podcast through iTunes.


Discoverability is one of the key issues that plagues the book and econtent world. The bad news is the situation is only going to get worse, particularly when you consider all the new publishing and self-publishing platforms that are vying for our attention. The good news is we’re starting to see platforms like Goodreads helping you discover new titles that match your interests. Patrick Brown, community manager at Goodreads, tells us all about their new recommendation engine and some of the complexities of the algorithm behind it. Key points from the full video interview (below) include:

  • Recommendation engines are complex: The Goodreads engine has been in development for six years. (In fact, the Goodreads algorithm benefited from the competition Netflix had to improve their own algorithm.) [Discussed at the 2:50 mark.]
  • The more you use it, the better the advice: Goodreads obviously wants us all to engage with their service as much as possible. One benefit to doing so is that the recommendations served up will be more fine-tuned to your interests. [Discussed at 5:24.]
  • Serendipity can be found further down the long tail: Part of what makes the Goodreads recommendation engine so valuable is that they’re not just recommending the latest bestseller on the topic. [Discussed at 6:40.]
  • Categories are broad today, but… : This initial release of the Goodreads recommendation engine uses large buckets (e.g., History, but not narrowed down to, say, WWII). Over time, the granularity, and therefore, the value of this aspect of the service will improve. [Discussed at 13:45.]

You can view the entire interview in the following video.

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