The question of man vs machine, physical vs online has always been a sensitive one. Whether the topic is why we should support independent bookstores instead of Amazon, why we love a print book more than an ebook, or why recommendations from a person is better than from an algorithm, there are always a lot of strong arguments and complex issues quoted. It's funny that man invented the machine to make his life simpler and he then turns against the machine because the latter is usurping him. Which is to be expected. Heck, I'm going to be severely annoyed if they invent a machine that can write code and design software products, because that's my job. So it's not hard to see the perspective from which the naysayers come. After all, we all have bills to pay and mouths to feed, right?
That said, when Goodreads announced the release of its new Netflix-style Recommendations system, I wasn't expecting much resistance. And true enough, there wasn't any brouhaha. Many, like me, were waiting for it to roll out from the time Goodreads announced its acquisition of Discovereads (the link basically redirects to Goodreads itself), in March 2011. Goodreads is my very favorite bookish site, the place that actually introduced me to book blogging, the site that linked me up with this hidden community of book-ravenous people. So their announcement of their new better-than-Amazon, something-like-Netflix recommendations system earned a big cheer from me.
Still, over the last couple of weeks, I have come across a few articles on the idea of recommendations itself - whether robots or humans are the best sources for picking our next book to read and how machine recommendations are lame when there are living breathing people who can do the same thing, with the added bonus of actually hearing them talk about the book to help us decide if it is the right thing for us. And that's exactly why I read book blogs. Especially the blogs, whose authors review books from the subjective point of view as opposed to the objective point of view. I read to learn more about different kinds of people, about history and its consequences, and also to learn about any topic under the sun, from ancient civilizations to viral diseases. But the reason I keep reading book after book, turning page after page, eagerly devouring the print word is because of the experience of reading itself. So when someone describes how wonderful a book made him/her feel, I am more inclined to read it.
I also listen to Books on the Nightstand podcast, whose 'Two Books I Can't Wait for you to Read' is my absolutely favorite moment of each episode. Some of the recommended books are probably not my usual fare, but the presenters - Michael Kindness and Ann Kingman - do a fabulous job of talking about the books that I still make it a point to read a few passages from the books.
But I love machine recommendations as well. Much as I won't hear the computer talk back to me about how moving or funny a book is, I like it that I don't have to explain my likes and dislikes and issues to someone before hearing him/her respond back with the perfect book for me. I like it that it does all the homework for me and tells me how Leila Aboulela's Lyrics Alley is something I may like because I enjoyed The London Train and The Secret Lives of Baba Segi's Wives. Besides, as Time puts it, "While peer recommendations are important, it's hard to argue against math". Math may have no feelings, but it's accurate. I also love how sometimes I can get choices that I have never heard of, and the element of surprise is retained when I actually pick the book to read, because I know absolutely nothing about it, and to the best of my knowledge none of my favorite bloggers have read it recently, so I feel that I may have a recommendation for them too. To me, peer and algorithmic recommendations go hand in hand, just like you look at Netflix for inspiration when you can't find a friend to talk to about movies.
Of course, Goodreads isn't the first book site to come up with a recommendations system. There have been many before it - What Should I Read Next?, Your Next Read and one of the most recent ones, Booklamp. But this is the one I've been most impressed with. The fact that Goodreads' recommendation algorithms not only look at the genre of books we enjoyed but also at user data (what other users thought of a book) probably has a lot to do with that. I tried to get a few recommendations today based on the three Newbery medal winners that I've already read so far. And out of the 24 recommendations the site threw at me, only three were titles I wasn't interested in, and even then only because they were books of poems. When I checked the literary fiction folder, there were a ton that has me eager to hit the library right now. There's something wonderful about seeing books you have probably never heard about, and that's what gets me excited about seeing mass online recommendations.
Do you like getting recommendations from a non-person?