During quarantine, most of the books I deemed as “classics” were written mostly by American authors, despite the fact that I grew up in the Philippines and was finishing my undergrad in Spain. I thought that it would be fun to explore authors from other cultural backgrounds as opposed to automatically flagging popular Western pieces as classics. 

In the process of trying to diversify my own shelf in the same way I look for new books, I realized I was trying to pull a comprehensive list of possible recommendations from really limiting or constrained data sets.



I was then reminded of Goodreads, the number one (partly because it’s the only one) cataloging app for books, and its extensive database of 1.5 billion books. I’ve been cataloging every book I’ve read since 2013, and as a (self-proclaimed) power user, the platform’s book discovery experience is not the most straightforward due to its (archaic lol) tagging system. 

I thought it would be so interesting if Goodreads offered a cool-looking automated feature that quickly and effortlessly served me with recommendations that diversified my existing shelf, in full knowledge of my reading preferences. So the question became: how can we use data from Goodreads as a tool to encourage people to diversify their shelves?