Multilevel modeling in Stan improves goodness of fit — literally.

John McDonnell sends along this post he wrote with Patrick Foley on how they used item-response models in Stan to get better clothing fit for their customers:

There’s so much about traditional retail that has been difficult to replicate online. In some senses, perfect fit may be the final frontier for eCommerce. Since at Stitch Fix we’ve burned our boats and committed to deliver 100% of our merchandise with recommendations, solving this problem isn’t optional for us. Fortunately, thanks to all the data our clients have shared with us, we’re able to stand on the shoulders of giants and apply a 50-year-old recommendation algorithm to the problem of clothes sizing. Modern implementations of random effects models enable us to scale our system to millions of clients. Ultimately our goal is to go beyond a uni-dimensional concept of size to a multi-dimensional understanding of fit, recognizing that each body is unique.

Cool! Their post even includes Stan code.

The post Multilevel modeling in Stan improves goodness of fit — literally. appeared first on Statistical Modeling, Causal Inference, and Social Science.