基于协作过的推算法对意见两极分化的影响.
Alessandro Bellina1,2, Claudio Castellano2,3, Paul Pineau4
1Dipartimento di Fisica Università "Sapienza," P. le A. Moro, 2, I-00185 Rome, Italy.
Physical review. E
|December 20, 2023
概括
推算法塑造了在线用户体验. 这项研究揭示了协作过如何导致两极分化或共识,确定了没有过泡的个性化建议的条件.
科学领域:
- 统计物理 统计物理
- 计算社会科学 计算社会科学
背景情况:
- 推算法通过策划内容显著影响在线用户体验.
- 虽然对内容发现有好处,但这些算法可以创建过器泡,可能会增加社会两极分化.
研究的目的:
- 调查用户-用户协作过推算法的对代理行为的影响.
- 分析系统动态,特别是相似性和人气偏见如何影响用户行为和系统状态.
主要方法:
- 分析和数值技术被用来模拟在重复暴露于算法的情况下的代理行为.
- 根据相似性和人气偏差的强度,我们得出了一个相位图来映射系统状态.
主要成果:
- 确定了三个不同的系统阶段:混乱,共识和两极分化.
- 极化的特点是用户分裂成聚焦于单个项目的群体.
- 在障碍极化边界发现了一个关键区域,可以实现非微不足道的个性化,而无需过气泡.
- 该模型成功地复制了在last.fm音乐平台上观察到的用户行为模式.
结论:
- 该研究为分析推算法提供了一个统计物理框架.
- 研究结果表明,理解偏差参数对于减轻两极分化至关重要.
- 这项研究为设计提供个性化内容的推系统开辟了道路,同时避免了过器泡和促进多样性.
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