Automated recommendations can nowadays be found on many online platforms, and such recommendations can create substantial value for consumers and providers. Often, however, not all recommendable items have the same profit margin, and providers might thus be tempted to promote items that maximize their profit. In the short run, consumers might accept non-optimal recommendations, but they may lose their trust in the long run. Ultimately, this leads to the problem of designing balanced recommendation strategies, which consider both consumer and provider value and lead to sustained business success. This work proposes a simulation framework based on Agent-based Modeling designed to help providers explore longitudinal dynamics of different recommendation strategies. In our model, consumer agents receive recommendations from providers, and the perceived quality of the recommendations influences the consumers' trust over time. In addition, we consider network effects where positive and negative experiences are shared with others on social media. Simulations with our framework show that balanced strategies that consider both stakeholders indeed lead to stable consumer trust and sustained profitability. We also find that social media can reinforce phenomena like the loss of trust in the case of negative experiences. To ensure reproducibility and foster future research, we publicly share our flexible simulation framework.

本文提出了一种基于代理模型的模拟框架，旨在帮助提供商探索不同推荐策略的纵向动态，以设计维持业务成功的平衡推荐策略，研究表明，在更多考虑客户效用的情况下，不忽视盈利能力的混合策略能够在长期中实现最高累计利润，同时社交媒体可以增强观察到的现象。

推荐系统的消费者价值与商业价值平衡：基于模拟分析