Goodreads wants to tell you what to read next

Library Journal calls the Netflix-like system great for book groups

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    Social media site Goodreads.com says it is better equipped than Amazon to offer truly customized book recommendations for readers.
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What book should you read next? Goodreads thinks it has some answers.

The online site, a book-focused social network, now provides Netflix-like recommendations based on readers’ previous picks and pans. Once users have rated 20 books, the system will start suggesting new possibilities to try. The algorithm is based on one from Discoverreads, a smaller company purchased by Goodreads in March, according to Library Journal.

Computerized recommendations are a big change for a site that’s previously been about what your friends are reading and praising. The early buzz is that the change is a plus, though, and that it’s about time the site turns the data from its 6 million members into a boon for readers rather than just advertisers.

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“While peer recommendations are important, it's hard to argue against math,” said Time.com. Library Journal thinks the recommendations will be a boon to book groups, always facing the question of what their members are collectively most likely to enjoy.

I gave the service a trial spin. For beginning users, it is of course a rough tool – 20 ratings was enough to give the system a good idea that I enjoyed classic children's literature, but not enough to translate that to new releases I haven’t yet encountered. I’m sure it means something that I’m among the few living readers not to appreciate Elizabeth Gilbert’s “Eat Pray Love,” but I think it’ll take Goodreads more data points to figure out just what that says about my tastes.

(The system did suggest I try Gabrielle Hamilton’s “Blood, Bones, and Butter,” a book I have already read. I was lukewarm on it at the start but loved the last third. Hard to translate that into stars.)

By relying on ratings rather than searches or purchases, Goodreads does come up with useful data. One of the shortcomings of Amazon’s system, for instance, is that my account isn’t just based on my personal likes; it’s skewed by my one-time searches for work, by my mom’s searches when she visits our house, by gift purchases, by my kids, and so on.

On Netflix, our recommendations reflect the odd mashup of my husband’s yen for fast-paced thrillers and my 4-year-old’s love for cartoon dinosaurs. (Washington Post critic Ron Charles obviously has a similar problem. He recently groused on Twitter that his "daughter's fondness for all things Ashton Kutcher has rendered Netflix's recommendation engine useless.")

Goodreads, however, claims that its service will be more directed, bragging that it has more data points and more nuance, using information such as how users categorize their books.

“Using ‘The Help’ as an example, if a reader liked it because they like reading historical fiction and they also liked ‘The Guernsey Literary and Potato Peel Society’ and ‘A Tree Grows in Brooklyn,’ then a great recommendation for that reader is ‘These Is My Words,’” the company said in a press release. “With Amazon, the focus is on other best sellers so someone buying ‘The Help’ would get recommendations for books as diverse as ‘Water For Elephants,’ ‘The Hunger Games’ and ‘One Day.’"

For me in the end, humans are still my best source of referrals. When my mom visits from across the country, she brings piles of books to read, and we spend her days here in running discussions of how we’re enjoying them. My friend and fellow food writer Nancy Leson has pointed me toward Bill Buford’s “Heat,” one of the great kitchen memoirs, and introduced me to Laurie Colwin’s essays. She convinced me I had to try The Zuni Cafe Cookbook and Anne Willan’s “Perfect Soups.”

I’m intrigued by Goodreads, enough to keep refining my ratings with them – but Mom and Nancy have a big human advantage over them: They not only know me well; they’re right there to loan me copies of the books that they think I’ll love.

Seattle writer Rebekah Denn blogs at eatallaboutit.com

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