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

The authors thank the senior editor, associate editor, and two anonymous reviewers for thoughtful suggestions throughout the review process; Alex Burnap, Xinyu Cao, Wesley R. Hartmann, Tongil TI Kim, Beibei Li, Pan Li, Song Lin, Nikhil Malik, Emaad Manzoor, Sridhar Narayanan, Ram Rao, Navdeep S. Sahni, Robbie Sanders, Shunyuan Zhang, and Yingjie Zhang, as well as seminar participants at Cornell University, UT Dallas, Hong Kong University of Science and Technology, the Chinese University of Hong Kong, Northwestern-Kellogg, Stanford GSB, Wharton, NYU Stern, UC Riverside, Temple University, USC Marshall, UChicago Booth, Peking University, University of Florida, University of Arizona, Berkeley Haas, Columbia Business School, UCLA Anderson, and the 2022 Marketing Science Conference, the 2022 INFORMS Annual Meeting, the Virtual Quant Marketing Seminar Series, the 2022 Conference on Information Systems and Technology (CIST), the 16th ACM Conference on Recommender Systems (Recsys 2022), the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), and the 2023 Summer Institute in Competitive Strategy (SICS) Conference for helpful suggestions on improving the paper; and Xiaoting Yin, Issac Liu, Ambika Krishnamachar, Yuanchi Ning, and Chen Peng at Uber Technologies for their engineering, product, and leadership support. All authors were employed and financially supported by Uber Technologies, Inc. (Uber) when the work was done. Uber recognizes a potential need to disclose certain confidential information and to protect such information from unauthorized use and disclosure. Uber had the right to remove its intellectual property or trade secrets subject to the following stipulations: (1) removing Uber confidential information from the paper, including data, code, and statistics such as conversion rate, number of impressions, clicks, orders per row, and so on and (2) compliance with Uber’s obligations as it relates to applicable laws, including the Data Protection Law, security laws, confidentiality requirements, and contractual commitments.