arXiv:0906.5286cs.IR
Putting Recommendations on the Map -- Visualizing Clusters and Relations
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
For users, recommendations can sometimes seem odd or counterintuitive. Visualizing recommendations can remove some of this mystery, showing how a recommendation is grouped with other choices. A drawing can also lead a user's eye to other options. Traditional 2D-embeddings of points can be used to create a basic layout, but these methods, by themselves, do not illustrate clusters and neighborhoods very well. In this paper, we propose the use of geographic maps to enhance the definition of clusters and neighborhoods, and consider the effectiveness of this approach in visualizing similarities and recommendations arising from TV shows and music selections. All the maps referenced in this paper can be found in http://www.research.att.com/~volinsky/maps
My first guess was that they used the artist.getSimilar API method. I dug around a little and found this: "In April 2009, we crawled the website [last.fm] by starting with Beethoven, and the top 20 musicians most similar to Beethoven, provided that each has at least 100,000…
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