Btw I just put something up online that I spent a couple of evenings in my couch putting together: it's a website where you can track any numerical data on the web. Want to know how many Twitter followers you have?
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A lot of people these days know about collaborative filtering. It's that Netflix Prize thing, right? People rate things 1-5 stars and then you have to predict missing ratings.
While there's no doubt that the Netflix Prize was successful, I think it created an illusion that all recommender systems care about explicit 1-5 ratings and RMSE as the objective.
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If you have a few minutes, you should check out mine and Chris Johnson‘s panel proposal. Go here and vote: http://panelpicker.sxsw.com/vote/24504
Algorithmic Music Discovery at Spotify
****Spotify crunches hundreds of billions of streams to analyze user's music taste and provide music recommendations for its users.
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I just answered a Quora question about what, if any, are the differences in the algorithms that are behind recommendations for music and movies.
Of course, every media type is different. For instance, there's fundamental reasons why latent factor models works really well for music and movies, as opposed to location recommendations where I suspect graph based models are more powerful.
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Andy Sloane decided to call my 2D visualization and raise it to 3D.
(Looks a little weird in the iframe but check out the link). It's based on a LDA model with 200 topics, so the artists tend to stick to clusters where each cluster is a topic.
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I'm at KDD in Chicago for a few days. We have a Spotify booth tomorrow, and I wanted to put together some cool graphics to show. I've been thinking about doing a 2D embedding of the top artists forever since I read about t-SNE and other papers so this was a perfect opportunity to spend some time on it.
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I've turned into a lazy bastard and I'm just posting presentations on this blog, but here's one from Rohan Singh at Spotify talking about the backend infrastructure of the Discover page.
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