Analysis for Feb 7 - Feb 13

This is the first week when last.fm threw up some recommendations from its recommender engine (Audioscrobbler). To summarize my efforts till now, I have setup last.fm clients on multiple machines and have been feeding my music preferences to Audioscrobbler. After playing close to 550+ songs and joining a music group on the network and adding some friends to my network, I have finally kick started my recommendation profile on last.fm

So what were results of all this? Recommendations … lots of them!!! Some very interesting aspects of these recommendations were:
1. the recommendations are classified based on where they have been derived from (group recommendations, friend recommendations, last.fm recommendations).

2. it looks like all these recommendations are tied up by Audioscrobbler to my listening preferences, but the recommendations vary very widely depending on the sources.

3. Recommendations from friends reflect their taste and I found most of these recommendations to be far from what I was looking for. But this is a social networking based technology, and listening to other people sometimes does open up interesting choices.

4. The recommendations are also classified based on which week they were produced. This is amazing, cause my taste can change from week to week, and so can that of my friends and groups. Thus the engine does not attempt to judge your preferences based on cumulative knowledge, but rather focuses on a weekly preferences and changes in listening habits too, though it does provide an option to get a list of recommendations based on cumulative listening habits.

5. Neighborhood of friends!!! Yes we can actually see all the people who are listening to music that u have in your profile. This neighborhood helps to showcase 2 things:
a. Popularity of the music you are listening to.
b. Potential friends who you can add to your list.

6. There is also a recommendation list for albums and concerts and other music related events, though I have not analyzed these recommendations in detail.

What I plan to do next.
1. Explore the tagging functionality and determine its influence on the recommendations generated by Audioscrobbler.
2. Can I make the system unlearn my preferences … which is very easy to do in Pandora?

There is a note on how recommendations are generated and tagging in the help section, which is where I plan to begin my hunt for more information. Also I plan to profile some of the recommendations to determine a pattern, if any, in the recommendations being generated.

Links:

1. to view my recommendations ... log into last.fm with the username zarthos@gmail.com and password 'zarthos'

2. my last.fm profile page