One obvious thing to anyone living in NYC is how tourists cluster in certain areas. I was curious about the larger patterns around this, so I spent some time looking at data. The thing I wanted to understand is: what areas are dominated by tourists?
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During my time at Spotify, I've reviewed thousands of resumes and interviewed hundreds of people. Lots of them were rejected but lots of them also got offers. Finally, I've also had my share of offers rejected by the candidate.
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From my presentation at MLConf, one of the points I think is worth stressing again is how extremely well combining different algorithms works.
In this case, we're training machine learning algorithms on different data sets (playlists, play counts, sessions) and different objectives (least squares, max likelihood).
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Just spent a day at MLConf where I was talking about how we do music recommendations. There was a whole range of great speakers (actually almost 2/3 women which was pretty cool in itself).
Here are my slides:
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Scrolling through the Discover page on Spotify the other day it occurred to me that the album is in fact a fairly strong visual proxy for what kind of content you can expect from it. I started wondering if the album cover can in fact be used for recommendations.
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So Luigi, our open sourced workflow engine in Python, just recently passed 1,000 stars on Github, then shortly after passed mrjob as (I think) the most popular Python package to do Hadoop stuff. This is exciting!
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In case you missed it, we just acquired a company called Echo Nest in Boston. These people have been obsessed with understanding music for the past 8 years since it was founded by Brian Whitman and Tristan Jehan out of MIT Medialab.
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