Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
This work addresses the impact of recommender systems on user diversity and homogenization, which is relevant for platform designers and researchers, but it is incremental as it builds on prior findings.
The paper tackles the problem of filter bubbles in recommender systems by modeling user decision-making through numerical simulation, finding that recommendation reduces within-user similarity but increases across-user homogeneity, creating a trade-off.
We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.