{
"title": "Audiomatch.net",
"keywords": ["music discovery", "music history", "similarity search"],
"date": "March 2004 - 2007",
"desc": "Music listening history and recommendation engine",
"creators": [{"name": "Neeraj Kumar", "role": "Lead Programmer"}, {"name": "Roy Kim", "role": "Databases and Front End"}],
"longdesc": "<p>With a proliferation of music artists working on blending various styles
of music, the use of traditional genres to describe an artist's creative
output is becoming increasingly less useful. In addition, people have
varied tastes and often like music in widely different genres.
Often, other people similarly like the same collection of
artists. Due to our strong interests in music, <a
href=\"http://roykim.net/\">Roy Kim</a> and I decided to create a system that
could recommend artists that would match with a listener's tastes. Thus,
<a href=\"http://audiomatch.net\">audiomatch.net</a> was born.</p>

<p>The basic idea is simple: users install a small plugin to their music
player which sends metadata about their currently playing song to our
servers. As a first level of service, users can access this history of
played songs and look at various statistics about their listening
preferences. In addition, they can generate dynamic text or images based
on their current song (e.g. for displaying on their webpages or AIM
profiles, etc.).</p>

<p>However, the more interesting use of the data comes from a backend
analyzer (written entirely by me), which looks at all the data and automatically
determines which artists are similar to each other based on a few
different techniques. This backend has been written with several
implementation issues in mind. Since we do not have the resources to
store all our data in memory at one time and do the processing on it,
the program has been written to process manageable chunks of data at a
time (in terms of both memory and execution time) and combine the
results together once each segment completes. Another major portion of
the program deals with incorrect data (of which there is a lot, since we
use ID3Tags from music, which are frequently incorrectly labelled).
The output from this backend program is then stored in a database which
the frontend uses to display personalized suggestions for each user.</p>",

"images": [{
    "url": "http://users.audiomatch.net/_/apu/audiomatch.png",
    "name": "Neeraj's current audiomatch status",
    "desc": "This is a live image showing my current audiomatch listening status."
}],

"relateds": [{
    "url": "http://audiomatch.net/",
    "name": "Audiomatch Website"
}]

}