eprintid: 10168775
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/16/87/75
datestamp: 2023-04-25 12:20:25
lastmod: 2023-04-25 12:21:27
status_changed: 2023-04-25 12:20:25
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Aridor, Guy
creators_name: Goncalves, Duarte
creators_name: Sikdar, Shan
title: Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F24
keywords: Filter Bubbles, Recommender Systems, Similarity-based Generalization
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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.
date: 2020-09
date_type: published
publisher: ACM
official_url: https://doi.org/10.1145/3383313.3412246
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2017740
doi: 10.1145/3383313.3412246
lyricists_name: Goncalves Dias Da Silva, Duarte
lyricists_id: DGONC71
actors_name: Goncalves Dias Da Silva, Duarte
actors_id: DGONC71
actors_role: owner
full_text_status: public
pres_type: paper
publication: Fourteenth ACM Conference on Recommender Systems
place_of_pub: New York, NY, USA
pagerange: 82-91
event_title: RecSys '20: Fourteenth ACM Conference on Recommender Systems
book_title: Proceedings of the 14th ACM Conference on Recommender Systems
citation:        Aridor, Guy;    Goncalves, Duarte;    Sikdar, Shan;      (2020)    Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems.                     In:  Proceedings of the 14th ACM Conference on Recommender Systems.  (pp. pp. 82-91).  ACM: New York, NY, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10168775/1/Aridor%20Goncalves%20Sikdar%202020%2C%20Deconstructing%20the%20Filter%20Bubble.pdf