Monday 10:00 AM–10:40 AM in Track 4

Diamond: mixed-effects models in Python

Timothy Sweetser

Audience level:
Intermediate

Description

Generalized linear mixed effects models, ubiquitous in social science research, are rarely seen in applied data science work despite their relevance and simplicity. We will discuss this class of statistical models, their usefulness in recommender systems, and present a fast, scalable Python solver for them called Diamond.

Abstract

intro to stitch fix (<5 min)

what is the mixed-effects model (5 min)

using mixed-effects models for recommender systems (10 min)

Computation (5 min)

Diamond (5 min)

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