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.

I’ve been considering a recommender system that attempts to identify individuals that are exemplars of a particular set of musical tastes. The data on clustering suggests that there are definite groupings of preference (which I’m betting map to genres). So, I’m hunting for the prototypical country listener or rap or whatever.

That idea has been done before to some extent. I know I’ve seen something on the idea of creating a sort of composite profile to represent a cluster, but I don’t know if I’ve read anything about attempting to identify an actual person who fits a cluster and then paying particular attention to that individual’s preferences.

In any case, I identify a set of exemplars. How many is a function of the data and how tight the clusters. With these people I create a system where these exemplars get positive social or emotional or financial feedback introducing new music to the system. Because their preference patterns are prototypical for the group, my reasoning is that the songs they pick will hit on whatever the key characteristics are and their recommendations will be “better” (as a function amount that a random user from the group will like the song).

Note that a convenient function of this system is that we aren’t trying to model what makes a song good even though our ultimate goal is to pick “good” songs to recommend. We are simply trying to model what makes a set of songs similar in human perception. Even if what actually makes a song good is not captured within the system, if that characteristic correlates to things that we are measuring then we can still get a correct grouping.

It’s a semantic point as much as anything, but it shifts the focus somewhat. It lends credence to the direction that Project Aura is headed because how much someone likes a song isn’t simply a function of the timbral or melodic characteristics. I know several feminists that don’t like rap. Honestly they have some pretty good arguments about misogyny and the objectification of women within that culture. A salient grouping characteristic though between a set of people and whether they like rap of not is going to be if they are feminists. That’s not the only axis, but it’s an example of a grouping that has nothing to do with the music itself that is useful if I am going to recommend a song to someone.

Continue reading →

This is one of the geekier memes I’ve seen, but I really like it. You’ll know what it means if it applies to you:

will@ebene:~$ history|awk '{a[$2]++} END{for(i in a){printf "%5d\t%s\n",a[i],i}}'|sort -rn|head
  169   fg
  138   make
   35   emacs
   29   svn
   26   cd
   24   ls
   20   for
   15   rm
    7   R
    6   mv

I found it from a fellow named Killus. I’ve been wandering the archive from his blog for a bit and I like the simple-but-complete math explanations theme. There’s quite a bit of neat stuff, but I’ll just mention a post on working for WRPI where, he argues (as a side note) that music recommenders will deviate from the public opinion simply by virtue of being so deeply involved in it.

I’ve got a note on that, but I’ll put it into another post to not melange these ideas.