Recommendations in a Marketplace


In recent years, two sided marketplaces have emerged as viable business models in many real world applications (e.g. Uber, AirBnb), wherein the platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. drivers, hosts). Such multi-sided marketplace involves interaction between multiple stakeholders among which there are different individuals with assorted needs. While traditional recommender systems focused specifically towards increasing consumer satisfaction by providing relevant content to consumers, two-sided marketplaces face an interesting problem of optimizing their models for supplier preferences, and visibility. In this tutorial, we consider a number of research problems which need to be address when developing a recommendation framework powering a multi-stakeholder marketplace, and provides audience with a profound introduction to this upcoming area and presents directions of further research. Tutorial material available at:


August 2020 | KDD

Bandit based Optimization of Multiple Objectives on a Music Streaming Platform

Rishabh Mehrotra, Niannan Xue, Mounia Lalmas

August 2020 | KDD

Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys

Rishabh Mehrotra, Ben Carterette, Yong Li, Quanming Yao, James Tin-Yau Kwok, Isabelle Guyon, Qiang Yang

August 2020 | KDD

Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions

Praveen Chandar, James McInerney, Brian Brost, Rishabh Mehrotra, Benjamin Carterette