Blog Recommenders

On the subject of my previous post about economic modeled music recommendations — I think a really good application of this would also be to blogging.

Imagine an app something like Google Reader, but where instead of me manually adding in bunches of feeds by myself, I log in and the program gives me a feed of items I am likely to like.

It’s related to the service that Stumble is doing, but collected in one place and with a more visible data model. Since the entrance to creating blog entries is lower than with music, you’d have a new factor. You’d have your audience, your exemplars and, if the application was popular, the bloggers would start to react as well.

Thinking about it in terms of blogging made me realize an assumption I made about music geeks. I assumed that a music geek would just start to slowly wider their horizons and start to like new genres. That the set of optimal songs for a given genre though would stay the same.

That’s not necessarily the case though. Imagine that as someone expands their musical horizons they begin to recognize good musical form. Peppy but sloppy songs they used to like may fall out of favor. Musicianship doesn’t always correlate to popularity.

Mathematically what this means is that a song isn’t simply a member of a single cluster because depending on the cluster the quality of that song will differ. A song is essentially in every cluster simultaneously to a varying (and frequently very small) amount. It suggests a different method for finding clusters by looking at patterns across axes rather than something like k-centroids that looks at all of them as a whole.