Abstract: Recommender systems are now ubiquitous in e-commerce. At Wayfair, we use a collection of machine learning models to predict which content and which products to show our customers at every stage in their shopping journey. Machine learning models for recommendations are often trained to optimize for near-term measures of customer satisfaction, such as clicks and conversion. However, there are often other business objectives that can appear to be in conflict with this idealized ranking. For example, some products might have a better margin than others, and surfacing those higher than they would normally be ranked could boost long-term profit. In this talk, I will give an overview of these competing business objectives, introduce the concept of Pareto Efficiency, and then discuss how we are addressing this problem at Wayfair for product recommendations.

About the Speaker: Ali Vanderveld is a Senior Staff Data Scientist at Wayfair, where she serves as a technical leader for machine learning, currently leading the development of novel search and recommendation technologies. Prior to Wayfair, she led a team focused on language AI at Amazon Web Services and was the Director of Data Science at ShopRunner. She has also worked at Civis Analytics, at Groupon, and as a technical mentor for the Data Science for Social Good Fellowship. Ali has a Ph.D. in theoretical astrophysics from Cornell University and got her start working as an academic researcher at Caltech, the NASA Jet Propulsion Laboratory, and the University of Chicago, working on the development teams for several space telescope missions, including ESA's Euclid.