TY  - GEN
PB  - ACM
A1  - Aridor, Guy
A1  - Goncalves, Duarte
A1  - Sikdar, Shan
KW  - Filter Bubbles
KW  -  Recommender Systems
KW  -  Similarity-based Generalization
SP  - 82
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
UR  - https://doi.org/10.1145/3383313.3412246
EP  - 91
ID  - discovery10168775
N2  - 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.
AV  - public
Y1  - 2020/09//
CY  - New York, NY, USA
TI  - Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
ER  -