.png){kind=icon}
返回
Managing Digital Platforms with Robust Multi-Sided Recommender Systems
.png){kind=icon}
PRE
AI
DOI：10.1080/07421222.2022.2127440.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Digital platforms
network effects
negative side effects
multi-sided platforms
multi-sided recommenders
robust optimization
agent-based simulation
期刊
I
IF:
8.2
论文数:
2.3K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏分享收藏
查看更多内容