It was probably for the best.

Lawrence Lessig has decided not to run for congress. As they said on draftlessig.org:

Lessig: No to Running for Congress, Yes to Changing Congress

Here's the video, it's fairly self explanitory:


I can understand the logic behind this. Really, Jackie Speier is extremely well liked in the whole state and certainly in the district as well and frankly she's a really good progressive politician (in so far as such a thing exists). So, with this in mind, Change Congress! Let's take some money out of politics.

Draft Lessig!

Draft Lessig!

I realize that many of the people reading this might not know who Larry Lessig is, so perhaps a brief intro, and then it should be clear to those who know me why he's exactly sort of congressperson I'd want (I'll take him in the Palo Alto district, the key is to get him into congress) Larry Lessig is the founder and ceo of the Creative Commons non profit set up to push the copyleft ideas of the same name. He is a law professor at the Stanford Law School, a founder of the school's Center for Internet and Society.Further, he is on the executive board of the Electronic Freedom Foundation and has worked in numerous ways to support the idea Free Software and Free Content in a general sense.

In response to this recent draft movement, Larry Lessig has officially started an exploratory committee. Watch this fantastic announcement video for more info about what his campaign would be about:







Change Congress!

h/t Matt Stoller over at Open Left

Trust in music recommendation

Lately I've been thinking a great deal about the role of trust in music recommendation. It seems from some informal case studies (I talked to some non-MIR folk I work with at the radio station) that one key piece of the music recommendation puzzle is the trust embedded in the relationship between the the receiver of recommends and the entity doing the recommendation. If you think about the traditional means of doing recommendations, this makes sense. A radio DJ develops a (trust) relationship with her/his listeners. This lends a great deal of weight to the recommendations that are made in this context and the likelihood of their retention.

So this leads to a few questions:

1. Has this 'trust effect' in recommenders been studied at all? I'd be very interested to se some good pysch research on the topic.
   
2. Assuming it really does exist, how do we, as developers of automated tools integrate such a thing into recommendation engines? Is it really as simple as making the 'best' recommendations possible? (I think probably no...)
   
3. Putting together (1) and (2) what effect does 'developing a relationship over time' have on the viability of a recommendation engine?
   

Anyway, I believe this is one of the critical issues to the success of music recommenders in improving beyond 'sounds like' and 'others like you enjoy' type of things to a more dynamic and comprehensive recommendation service.

MyPySpace update

I have finally set down an intial release of the musicGrabber tool, which is being developed by myself and kurtjx over on sourceforge. You can now grab the python tool without getting your hand dirty in our subversion repository. Go take a look

A division of posting...

I think in the name of clarity I'll be using this space to post work and research related things, while leaving my original blog for personal things. I think this will be useful in the future.

right. so the thing is, I don't really post here.

If you're really interested in my thoughts on stuff along with things, you may be interested in reading my actual blog though.