Machine Learning for (e-)Commerce

The goal of this workshop is to study the challenges in learning, evaluating, and mining of e-commerce and more classical commerce domains. As the largest commerce and e-commerce companies on the planet are adopting machine learning technologies, it becomes increasingly clear that these domains present different challenges that classical machine learning problems.

In this workshop we plan to focus on the problems more than on solutions. We will consider problems such as identifying dysfunctional items or collections in a website, off-policy evaluation of marketing strategies, personalization of e-commerce experience, validation, sequential decisions, dynamic pricing, and others. Our main goal is to portray the main challenges of the field and to propose an industry-academia agreed collection of benchmarks problems for theoretical study and experimental work.