Managing Digital Platforms with Robust Multi-Sided Recommender Systems

Digital platforms have replaced traditional markets in most industries and orchestrate socioeconomic aspects of our lives. We address the problem of negative direct side network effects that arise with an increased number of agents on one side of the platform. Negative effects, if unaddressed, lead to undesired long-term consequences for the platform by developing a positive vicious cycle. Addressing negative effects require dynamic solution mechanisms that adapt to the changing landscape of platforms. The recommender systems literature has proposed multi-sided recommender systems (MSR) as a dynamic solution to many problems on platforms. However, current state-of-the-art MSRs do not consider uncertainty in predicting agents' choices, resulting in limited efficacy. We present a robust multi-sided recommender system that considers estimation errors in agents' choice to address this concern. Extensive experiments with agent-based models-ride-pooling and education platform-provide support for the efficacy and generalizability of the robust MSR to address negative effects.

Automated summary

Digital platforms have replaced traditional markets and play a significant role in our socioeconomic aspects. However, negative direct side network effects on these platforms can have undesired consequences. To address this, dynamic solution mechanisms, such as multi-sided recommender systems, have been proposed. Current systems, however, fail to consider uncertainty in predicting agents' choices, limiting their efficacy. In this study, a robust multi-sided recommender system is presented, considering estimation errors in agents' choices, and experiments show its effectiveness and generalizability in addressing negative effects.