Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems naturally influences both users and creators: users' preferences are affected by the items they are recommended, while creators may be incentivized to alter their content to attract more users. We define a model, called user-creator feature dynamics, to capture the dual influence of recommender systems. We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system. We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-$k$ truncation can prevent polarization and improve diversity of the system.

翻译：推荐系统具有双重目的：向用户呈现相关内容，同时帮助内容创作者触达目标受众。这种双重性质自然会影响用户和创作者：用户的偏好受推荐内容影响，而创作者可能被激励调整其内容以吸引更多用户。我们定义了一个称为用户-创作者特征动态的模型，以捕捉推荐系统的双重影响。我们证明，具有双重影响的推荐系统必然导致极化，造成系统多样性损失。随后，我们从理论和实证两方面研究了缓解极化、促进推荐系统多样性的方法。出乎意料的是，我们发现常见的多样性促进方法在双重影响下无效，而像top-$k$截断这类相关性优化方法反而能防止极化并提升系统多样性。