Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach

Yuyan Wang (), Long Tao () and Xian Xing Zhang ()
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Yuyan Wang: Stanford Graduate School of Business, Stanford, California 94305
Long Tao: Uber Technologies, Inc., San Francisco, California 94158
Xian Xing Zhang: Uber Technologies, Inc., San Francisco, California 94158

Marketing Science, 2025, vol. 44, issue 1, 1-29

Abstract: 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.

Keywords: recommender systems; multi-sided marketplace; food-delivery platforms (search for similar items in EconPapers)
Date: 2025
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