Recommender systems play a vital role in driving the long-term values for online platforms. However, developing recommender systems for multi-sided platforms faces two prominent challenges. First, recommending for multi-sided platforms typically involves a joint optimization of multiple, potentially conflicting objectives. Second, many platforms adopt hierarchical homepages, where items can either be individual products or groups of products. Off-the-shelf recommendation algorithms are not applicable in these settings. To address these challenges, we propose MOHR, a novel multi-objective hierarchical recommender. By combining machine learning, probabilistic hierarchical aggregation, and multi-objective optimization, MOHR efficiently solves the multi-objective ranking problem in a hierarchical setting through an innovative formulation of probabilistic consumer behavior modeling and constrained optimization. We implemented MOHR at Uber Eats, one of the world’s largest food delivery platforms. Online experiments showed significant improvements in consumer conversion, retention, and gross bookings, resulting in a $1.5 million weekly increase in revenue. Moreover, MOHR offers managers a mathematically principled tool to make quantifiable and interpretable trade-offs across multiple objectives. As a result, it has been deployed globally as the recommender system for Uber Eats’ app homepage.
