|Large network with multiple clusters: based on consumer choices|
The network graph above reveals clusters of similar books sold by Amazon. Today, Amazon knows how products they sell cluster, will they soon know how customers cluster, in non-obvious ways?
Amazon, with their public/private highlights/notes from Kindle readers is creating a knowledge & interests ecosystem that will aggregate what the world is interested in, and what the world finds important... and what the world wants to buy more of. It is not just the also-bought data that matters (which books bought by same customer), it is what we specifically find interesting and useful in those books that reveals deep similarities between people -- the hi-lites, bookmarks and the notes will be the connectors. With electronic hi-lites, bookmarks, and notes for Kindle books Amazon is raking in the data. Our choices reveal who we are, and who we are like!
It is what we specifically find interesting and useful in those books that reveals deep similarities between people. We will connect with each other via our similarities and profit from our differences... and so will Amazon!
Below is a network map(via social network analysis) of a very interesting book -- Too Big to Know [2B2K] by internet scholar David Weinberger. David's book is shown by the magenta node in the center of the network. Directly connected to his book are the books that Amazon mentions that customers also bought [green nodes], in addition to 2B2K. These books are probably more similar than different to 2B2K. The blue nodes are books that are 2 steps away from 2B2K, they are probably more different than 2B2K, but retain strong similarities. The arrows show the direction of the majority of also-bought activity. If you find 2B2K interesting, you will probably find a pleasant read in one of green books or possibly a blue book -- depending upon your desire for difference.
Book Network Neighborhood for Too Big to Know
With Kindle readers providing electronic hi-lites, notes and bookmarks, Amazon is getting much more data on individual interest graphs. Today, Amazon introduces you to similar books. Tomorrow, they will introduce you to similar readers?