Summary of Uncovering the Deep Filter Bubble: Narrow Exposure in Short-video Recommendation, by Nicholas Sukiennik et al.
Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation
by Nicholas Sukiennik, Chen Gao, Nian Li
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A study investigates the “deep filter bubble” phenomenon on short-video platforms, where users are exposed to narrow content within their broad interests. Researchers analyzed one-year interaction data from a top Chinese platform, finding that while the overall proportion of users in a filter bubble remains constant over time, the depth composition of their filter bubble changes. The study also identifies factors contributing to this phenomenon, such as specific categories, user demographics, and feedback type. To reduce the risk of users getting caught in a bubble, the authors propose design strategies for recommender systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Filter bubbles on short-video platforms can cause problems like user dissatisfaction or polarization. Researchers looked at one-year data from a popular Chinese platform to understand this issue better. They found that some users get stuck in a “deep filter bubble” where they only see narrow content. The study shows how the depth of these bubbles changes over time and which factors contribute to them, like what kind of videos people watch or what feedback they give. To make things better, the researchers suggested ways to design recommender systems that don’t trap users in a filter bubble. |
