Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
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3589334.3645497.pdf
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1.29 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Anwar, Md Sanzeed
Schoenebeck, Grant
Dhillon, Paramveer S.
Date Issued
May 13, 2024
Publisher
ACM
Citation
Anwar, Md Sanzeed, Schoenebeck, Grant and Dhillon, Paramveer S. 2024. "Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns."
Version
Final published version
Abstract
Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals with differing preferences only consume content aligned with their preferences (without much overlap with other users). Prior research assumes a trade-off between homogenization and filter bubble effects and then shows that personalized recommendations mitigate filter bubbles by fostering homogenization. However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects. We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics: how different the average consumption is between users (inter-user diversity) and how varied an individual's consumption is (intra-user diversity). We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity.
Description
WWW '24: Proceedings of the ACM on Web Conference May 13–17, 2024, Singapore, Singapore
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3589334.3645497
