  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.
