Data Piques

Mar 20, 2017

Feb 05, 2017

Dec 05, 2016

Nov 07, 2016

Learning to Rank Sketchfab Models with LightFM

In this post we're going to do a bunch of cool things following up on the last post introducing implicit matrix factorization. We're going to explore Learning to Rank, a different method for implicit matrix factorization, and then use the library LightFM to incorporate side information into our ...

Oct 19, 2016

Oct 09, 2016

Likes Out! Guerilla Dataset!

-- Zack de la Rocha

tl;dr -> I collected an implicit feedback dataset along with side-information about the items. This dataset contains around 62,000 users and 28,000 items. All the data lives here inside of this repo. Enjoy!

In a previous post, I wrote about how to use matrix ...

Aug 30, 2016

Jul 20, 2016

I'm all about ML, but let's talk about OR

You've studied machine learning, you're a dataframe master for massaging data, and you can easily pipe that data through a bunch of machine learning libraries.

You go for a job interview at a SAAS company, you're given some raw data and labels and asked to predict churn ...

Jan 09, 2016

Explicit Matrix Factorization: ALS, SGD, and All That Jazz

In my last post, I described user- and item-based collaborative filtering which are some of the simplest recommendation algorithms. For someone who is used to conventional machine learning classification and regression algorithms, collaborative filtering may have felt a bit off. To me, machine learning almost always deals with some function ...

Nov 02, 2015

Intro to Recommender Systems: Collaborative Filtering

I've written before about how much I enjoyed Andrew Ng's Coursera Machine Learning course. However, I also mentioned that I thought the course to be lacking a bit in the area of recommender systems. After learning basic models for regression and classification, recommmender systems likely complete the triumvirate ...

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