Multi-stakeholder Machine Learning for Marketplaces
Multi-stakeholder Machine Learning for Marketplaces


In recent years, two sided marketplaces have emerged as viable business models in many real world applications (e.g. Amazon, AirBnb, Spotify, YouTube), wherein the platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. retailer, artists). 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 the consumers, two-sided marketplaces face an interesting problem of optimizing their models for supplier preferences, and visibility.

In this tutorial styled talk, we consider a number of research problems which need to be address when developing a recommendation framework powering a multi-stakeholder marketplace. The talk provides the audience with a profound introduction to this upcoming area and presents directions of further research.

We begin by contrasting traditional recommendations systems with those needed for marketplaces, and identify four key research areas which need to be addressed. First, we highlight the importance of a multi-objective ranking/recommendation module which jointly optimizes the different objectives of stakeholders while serving recommendations. Second, we discuss different ways in which stakeholders specify their objectives, and highlight key issues faced when quantifying such objectives. Third, we discuss user specific characteristics (e.g. user receptivity) which could be leveraged while jointly optimizing business metrics with user satisfaction metrics. Furthermore, we highlight important research questions to be addressed around evaluation of such systems. Finally, we end the tutorial by discussing various different case studies: Airbnb, Uber, Turo, Spotify, and highlight recent findings.


Rishabh Mehrotra is a Senior Research Scientist at Spotify Research in London. He obtained his PhD in the field of Machine Learning and Information Retrieval from University College London where he was partially supported by a Google Research Award. His PhD research focused on inference of search tasks from query logs and their applications. His current research focuses on bandit based recommendations, counterfactual analysis and experimentation. Some of his recent work has been published at top conferences including WWW, SIGIR, NAACL, CIKM, RecSys and WSDM. He has co-taught a number of tutorials at leading conferences (WWW & CIKM) & was recently invited to teach a course on “Learning from User Interactions” at the Russian Summer School on Information Retrieval and the ACM SIGKDD Africa Summer School on Machine Learning for Search.

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