Uplift Modeling

By Alejandro Correa and Maria Fernanda Cortes  This post is part of a series in which I’m discussing several parts of my AI_at_Rappi presentation. In a previous post I discussed a particular algorithm for recommending restaurants called rest2vec, In a follow-up, I discussed how to include financial costs when analyzing a churn model.  This time...

Maximizing a churn campaign’s profitability with cost-sensitive Machine Learning, part 3

This post is part of a series in which I'm discussing several parts of my AI_at_Rappi presentation. In the last two posts, we first discussed how to evaluate a churn marketing campaign using a financial evaluation measure and then how to estimate the customer lifetime value and also how it is possible to design experiments...

Maximizing a churn campaign’s profitability with cost-sensitive Machine Learning, part 2

This post is part of a series in which I'm discussing several parts of my AI_at_Rappi presentation. In the latest post, we discussed how to evaluate a churn marketing campaign using a financial evaluation measure. In this one, we're going to deep down in a couple of important concepts needed to fully being able to...

Maximizing a churn campaign’s profitability with cost-sensitive Machine Learning, part 1.

This post is part of a series in which I'm discussing several parts of my AI_at_Rappi presentation. In a previous post I discussed a particular algorithm for recommending restaurants called rest2vec. This time I wanted to talk about how to model customer churn using cost-sensitive machine learning. Churn modeling The two main objectives of subscription-based...

Rest2Vec: Recommending similar restaurants

It has been almost a year since I joined the Rappi Artificial Intelligence team. It has been a blast, lots of highly complex challenges with big rewards. As part of my role, I have been giving talks on countless occasions, and even if I have to adjust my speech according to each audience background, I...

Building AI Applications Using Deep Learning

Recently, we have seen a huge boom around the field of deep learning; it is currently being implemented in a wide variety of fields, from driverless cars to product recommendation. In their most primitive form, deep learning algorithms originated in the 1960s. If the concept has been around for decades, why is it that widespread...