  Recommender systems, which offer personalized suggestions to users, power
many of today's social media, e-commerce and entertainment. However, these
systems have been known to intellectually isolate users from a variety of
perspectives, or cause filter bubbles. In our work, we characterize and
mitigate this filter bubble effect. We do so by classifying various datapoints
based on their user-item interaction history and calculating the influences of
the classified categories on each other using the well known TracIn method.
Finally, we mitigate this filter bubble effect without compromising accuracy by
carefully retraining our recommender system.
