Optimizing Recommendations for Competing Business Objectives


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 typically 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 higher margins or lower shipping costs than others, and surfacing such products higher than they would normally be ranked could potentially boost long-term profit. However, such profit boosts will likely come with a decrease in short term clicks and conversion. Thus we need to find ways to balance these competing objectives if we want to make our product recommendations ""profit aware.""

In this talk I will give an overview of this problem, what makes it difficult, how we have been addressing it at Wayfair, and the lessons that we have learned so far. I will start by giving an overview of the different types of competing business objectives that can arise in the e-commerce use case and how they compete against each other in practice. Along the way I will introduce some of the fundamental concepts of multi-objective optimization, including Pareto Efficiency and the Pareto Frontier, and how they relate to this problem. Finally, I will discuss the pros and cons of various strategies for making recommendation systems profit aware.


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 PhD 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.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google