Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals with differing preferences only consume content aligned with their preferences (without much overlap with other users). Prior research assumes a trade-off between homogenization and filter bubble effects and then shows that personalized recommendations mitigate filter bubbles by fostering homogenization. However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects. We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics: how different the average consumption is between users (inter-user diversity) and how varied an individual's consumption is (intra-user diversity). We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity.

翻译：推荐算法在塑造媒体选择中扮演关键角色，因此理解其对用户行为的长期影响至关重要。这些算法通常与两种关键结果相关联：同质化，即用户尽管潜在偏好不同，却消费相似内容；以及过滤泡沫效应，即偏好不同的个体仅消费与其偏好一致的内容（用户间重叠度低）。现有研究假定同质化与过滤泡沫效应之间存在权衡关系，进而表明个性化推荐通过促进同质化来缓解过滤泡沫。然而，由于这种权衡假设，现有工作难以更细致地分析推荐系统如何独立影响同质化与过滤泡沫效应。我们通过将这两种效应分解为两个关键指标——用户间平均消费差异度（用户间多样性）与个体消费内容差异度（用户内多样性）——提出了更精确的定义。随后，我们采用一种新颖的基于智能体的仿真框架，以整体视角评估推荐系统对同质化与过滤泡沫的影响。仿真结果表明，传统推荐算法（基于历史行为）主要通过影响用户间多样性来减少过滤泡沫，而对用户内多样性的影响不显著。基于此，我们提出了两种新的推荐算法，通过兼顾两种多样性指标，采取更细致的优化策略。