Session 5: Recommending for a Multi-Sided Marketplace with Heterogeneous Contents

Описание: RecSys 2022 by Yuyan Wang (Uber Tech. Inc, United States), Long Tao (Uber Tech. Inc, United States), Xian Xing Zhang (Uber Tech. Inc, United States)

Many online personalization platforms today are recommending heterogeneous contents in a multi-sided marketplace consisting of consumers, merchants and other partners. For a recommender system to be successful in these contexts, it faces two main challenges. First, each side in the marketplace has different and potentially conflicting utilities. Recommending for a multi-sided marketplace therefore entails jointly optimizing multiple objectives with trade-offs. Second, the off-the-shelf recommendation algorithms are not applicable to the heterogeneous content space, where a recommendation item could be an aggregation of other recommendation items. In this work, we develop a general framework for recommender systems in a multi-sided marketplace with heterogeneous and hierarchical contents. We propose a constrained optimization framework with machine learning models for each conflicting objective as inputs, and a probabilistic structural model for users’ engagement patterns on heterogeneous contents. Our proposed structural modeling approach ensures consistent user experience across different levels of aggregation of the contents, and provides levels of transparency to the merchants and content providers. We further develop an efficient optimization solution for ranking and recommendation in large-scale online systems in real time. We implement the framework at one of the largest online food delivery platforms in the world, which is a three-sided marketplace consisting of eaters, restaurant partners and delivery partners. Online experiments demonstrate the effectiveness of our framework in ranking heterogeneous contents and optimizing for the three sides in the marketplace. Our framework has been deployed globally as the main recommendation algorithm for the homepage of the platform.