Balanced News Using Constrained Bandit-based Personalization
Balanced News Using Constrained Bandit-based Personalization
复制标题
使用基于 Bandit 的受限个性化平衡新闻
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
L. E. Celis
中科院分区：
文献类型：
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作者：
Sayash Kapoor;Sayash Kapoor;Vijay Keswani;Nisheeth K. Vishnoi;L. E. Celis
我们提出了一个新闻搜索引擎的原型，提出了平衡的观点，在自由和保守的文章，去极化内容的目标，让用户逃离他们的过滤泡沫。平衡是根据灵活的用户定义的约束，并利用最新的进展约束强盗优化。我们通过将其与传统（极化）提要产生的新闻提要并排显示来展示我们的平衡新闻提要。
We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of depolarizing content and allowing users to escape their filter bubble. The balancing is done according to flexible user-defined constraints, and leverages recent advances in constrained bandit optimization. We showcase our balanced news feed by displaying it side-by-side with the news feed produced by a traditional (polarized) feed.