Recommender systems play a vital role in driving the long-term value for online platforms. However, developing recommender systems for multi-sided platforms faces two prominent challenges. First, different sides have different and possibly conflicting utilities. Recommending in this context entails jointly optimizing multiple 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 on one of the world’s largest food delivery platforms, and demonstrate that long-term profit maximization can be achieved through a multi-objective approach as we proposed, outperforming existing single-score based approaches. Moreover, the MOHR framework offers managers a mathematically principled tool to make quantifiable and interpretable trade-offs across multiple objectives for long-term profit optimization. Online experiments showed significant improvements in consumer conversion, retention, and gross bookings, resulting in a \$1.5 million weekly increase in revenue. As a result, MOHR has been deployed globally as the recommender system for the food delivery platform’s app homepage.