Try, Learn, Buy: Operations Research Meets AI

Data Science Ballroom AB
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In this talk we will look at how operations research can be married with domain knowledge and machine learning to solve “the Birchbox problem”.

At the core of Birchbox’s subscription service lies an optimization problem. Birchbox is a discovery platform for products. Birchbox subscribers receive boxes of samples, which they can try, learn about via our editorial content, and purchase full-size versions of in our store.

Each month we design a number of boxes (e.g., 30) and then assign the boxes to our customers by encapsulating our business rules and objectives into an optimization problem. There are a variety of hard constraints that we consider, including those derived from customer profile attributes and sampling history (e.g. must not receive the same sample twice). We use soft-constraints, which express how suitable we think each box is for each customer, to incorporate domain knowledge and statistical inference. The optimization problem is to ensure that each customer is assigned a box, subject to supply constraints for each box, while maximizing the “reward” expressed by the soft-constraints. The notion of “reward” encoded in the soft-contrainsts can be quite complex, incorporating infered notions of customer happiness, likelihood of conversion to full-size sale, effect on customer retention etc.

We will discuss how we tackle the box allocation problem in practice including how we formulate it as a variant of the well-known transportation problem (resulting in a matrix of more than a trillion entries), our plans for future work, and how the techniques we use can be applied to other domains.

Liz Crawford

Genacast

Liz is the CTO at Birchbox an ecommere company that is changing the way people discover products. She has published papers on topics ranging from Machine Learning and Natural Language Processing to Intelligent Tutoring Systems and Robotics. Liz has a PhD in Computer Science from Carnegie Mellon and is a co-organizer of CTO School.

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