Good recommendation algorithms are really (really!) difficult to do right. We built Goodreads so that you could find new books based on what your friends are reading, and now we want to take the next step to make that process even more fruitful.
This quotation is from the Goodreads blog, a post written by Otis Chandler, a Goodreads CEO. The “next step” to which he refers is Goodreads’ acquisition of the small start-up, Discovereads, which was developing algorithms around book recommendations. The algorithms used by Discovereads were multiple, based on book ratings from millions of users, and tracking data patterns of how people read, how they rate, the choices they make, what might influence them.
It’s roughly based on the sorts of algorithms that drive Netflix, though there’s an obvious difference between the two platforms, and it’s not the type of content. Goodreads isn’t a publisher nor a producer of its own content; it isn’t promoting its own creations but rather can influence the user to spend money in a way that Netflix, which works to a different economic model, may not. Chandler admits this: one of the goals in adopting the Discovereads algorithm is that it will improve marketing strategies, ensuring that sponsored content (books promoted to users) will be more up their street.
Given this, then, it’s possible to say that the way recommendations work in Goodreads is based on at least three things:
- The ratings provided by an individual at the point they sign up – part of the process of getting a Goodreads account is adding genres you’re interested in, and “rating” a (computer-generated) series of books
- The algorithms at play are monitoring human patterns of reading and rating and, presumably, analytics and big data collected on what might encourage a person to add a recommended book to their lists (and perhaps, too, to their shopping basket)
- The Amazon connection: the fact that Goodreads isn’t providing its own content, and that it’s owned by Amazon, makes a particular sort of economic link. Not only does it incentivise Goodreads promoting specific economic content, but it means that Goodreads can influence how and where consumers’ money is spent. Presumably analytics based on how often Goodreads’ recommendations leads to a purchase is fed back into the recommendation system to improve upon it.
Knox (2015) suggests that actor-network theory might account for the “layers of activity involved” in the complex, often hidden, and often automated ways in which humans and non-humans interact in the development and deployment of algorithms. One of the principal benefits of this approach (and there are many) is that it inherently assumes that the human and non-human are working together. This is not always self-evident, and the quotation at the top of this post suggests that the two are seen to be in opposition. The incorporation of the Discovereads algorithm, it is implied, will lead to a fundamentally different way of generating recommendations. It signals a move from human-generated recommendations (what your friends are reading) to computer-generated ones, based on this algorithm.
The responses to the blog post written by Chandler suggest that this binary is presupposed by Goodreads users as well. The posts below, for example, clearly espouse the benefits of both ‘routes’ to recommendations. But they suggest that recommendations are either human- or computer-generated: there’s no indication that non-human interference in extant friend-generated recommendations, nor any human influence in the computer-generated ones. It’s a code-based version of the binary we’ve encountered lots in the past eight weeks: the perception that the options of technological instrumentalism and technological determinism are the only ones.
The reality, of course, is that it’s a false binary. It’s not a choice of human or non-human but – as Knox outlines – both are present. The difference, then, to which Chandler refers, the change heralded by the acquisition of Discovereads, isn’t necessarily in the source of the content, but in the perception of that source. It’s in the perceived transparency or hiddenness of the algorithm.