Bursting Filter Bubbles With Serendipity

When talking about personalization online, Google CEO Eric Schmidt recently said "it will be very hard for people to watch or consume something that has not in some sense been tailored for them." This level of personalized filtering of content has worried academics and activists. Many argue that users will be trapped in a so-called "Filter Bubble," limiting their exposure to challenging or new ideas they are not expected to like. In answer to these worries, serendipitous recommendation systems have been developed to help users make surprising and pleasant discoveries outside of their typical online content sphere. In this thesis, we investigate whether serendipitous recommendation system do indeed help break the filter bubble effect. In contrast to previous work, we investigate this question on one large-scale live experiment. We find that serendipity partially mitigates the filter bubble effect, but that users are more responsible for their own filter bubbles than algorithms. Further, we search for user characteristics that can be used to identify users more likely to experience the filter bubble effect. We find that users spending more time on site are less likely to experience the filter bubble effect.Electrical Engineering, Mathematics and Computer ScienceSoftware Technolog