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This awesome talk by Chris Johnson and Edward Newett, machine learning engineers at Spotify, shows how they imagined, tested, iterated and built the highly-popular "Discover Weekly" feature of Spotify from start to finish.

Learn how product-oriented engineers think in this talk and the tradeoffs they make as they're looking for ways to rapidly test ideas and iterate. Of course, this product was built on music recommendations, so you'll also get to see how they thought through the process to figure out exactly how to generate meaningful recommendations for their millions of users.

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This talk was a talk recorded at the DataEngConf 2015 event in NYC.

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Tom Pinckney of eBay discusses eBay's new graph-based recommendation engine. The first part of the talk is spent on discussing the theory behind the recommendation engine and the conceptual pieces of how it works.

The second part of the talk is spent on discussing eBay's implementation of Cassandra to implement the recommendation engine. In particular, the focus is on how eBay is able to use Cassandra to handle the tremendous scale that eBay needs for this kind of a recommendation system. As you can imagine, eBay has a tremendous amount of data that is constantly coming in. In using Cassandra, Tom explains how eBay is able to handle that load.


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In this talk, we'll see how recommendation systems are created from data. What's the algorithm? What's the evaluation method? What's the optimization procedure? When does it converge? We'll talk about parallelizing in order to scale up to "big data" size via the MapReduce framework. Finally, we'll think about priors and how they are overloaded. Content from this talk draws from chapters in Doing Data Science contributed by David Crawshaw and Matt Gattis. This talk was recorded at the NYC Machine Learning meetup at Pivotal Labs.


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John Jensen and Mike Sherman will be speaking about their problem domain over at Rich Relevance . At Rich Relevance, they provide content personalization as a service, mostly to retailers. Unlike Pandora, they don't use intrinsic similarity metrics with in-depth knowledge about the domain they are recommending. This talk was recorded at the SF Data Mining meetup at Pandora HQ.


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