All-pairs similarity via DIMSUM
We are often interested in finding users, hashtags and ads that are very similar to one another, so they may be recommended and shown to users and advertisers. To do this, we must consider many pairs of items, and evaluate how “similar” they are to one another. We call this the “all-pairs similarity” problem, sometimes known as a “similarity join.” We have developed a new efficient algorithm to so
Recommending music on Spotify with deep learning
This summer, I’m interning at Spotify in New York City, where I’m working on content-based music recommendation using convolutional neural networks. In this post, I’ll explain my approach and show some preliminary results. Overview This is going to be a long post, so here’s an overview of the different sections. If you want to skip ahead, just click the section title to go there. Collaborative fil
Erik Bernhardsson
Want to get blog posts over email? Enter your email address and get an email (roughly monthly) when there's a new post! ... is the founder of Modal Labs which is working on some ideas in the data/infrastructure space. I used to be the CTO at Better. A long time ago, I built the music recommendation system at Spotify. You can follow me on Twitter or see some more facts about me.
Netflix Update: Try This at Home
[Followup to this] Ok, so here's where I tell all about how I (now we) got to be tied for third place on the netflix prize. And I don't mean a sordid tale of computing in the jungle, but rather the actual math and methods. So yes, after reading this post, you too should be able to rank in the top ten or so. Ur... yesterday's top ten anyway. My first disclaimer is that our last submission which tie
GraphLab: Collaborative filtering library using matrix factorization methods
GraphLab collaborative filtering library: efficient probabilistic matrix/tensor factorization on multicore This webpage explains how to use GraphLab collaborative filtering library. In this library, multiple matrix decomposition algorithms are implemented. See description in the following papers: Probablistic matrix/tensor factorization: A) Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, J
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http://www.cs.toronto.edu/~fritz/absps/netflix.pdf