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Preprints, Working Papers, ... Year : 2023

Browsing Amazon's Book Bubbles

Abstract

This study investigates Amazon’s book recommendation system, uncovering cohesive communities of semantically similar books. The confinement within communities is extremely high, a user following Amazon’s recommendations needs tens of successive clicks to navigate away. We identify a large community of recommended books endorsing climate denialism, COVID-19 conspiracy theories, "New World Order" narratives, and advocating conservative views on social and gender issues. Performing a collaborative filtering analysis, relying on Amazon users reviews, reveals that books reviewed by the same users tend to be co-recommended by Amazon. This study not only contributes to addressing a gap in the literature by examining Amazon’s recommender systems, but also highlights that even non-personalized recommender systems may pose systemic risks by suggesting content with foreseeable negative effects on public health and civic discourse.
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Dates and versions

hal-04308081 , version 1 (26-11-2023)

Identifiers

  • HAL Id : hal-04308081 , version 1

Cite

Paul Bouchaud. Browsing Amazon's Book Bubbles. 2023. ⟨hal-04308081⟩
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