论文标题
隐藏的作者偏见在书中推荐
Hidden Author Bias in Book Recommendation
论文作者
论文摘要
协作过滤算法的优点是不需要敏感用户或项目信息来提供建议。但是，他们仍然遭受与公平相关的问题的困扰，例如受欢迎程度偏见。在这项工作中，我们认为，当未向研究人员提供其他用户或项目信息时，流行偏见通常会导致其他偏见。我们在书籍中使用书面评级的数据集上的建议案例中检查了我们的假设。我们使用公开可用的外部资源将其丰富了作者信息。我们发现流行的书籍主要是由美国公民在数据集中撰写的，并且与用户的配置文件相比，流行的协作过滤算法往往会不成比例地推荐这些书籍。我们得出的结论是，学者社区应进一步研究受欢迎程度偏见的社会含义。
Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness related issues, like popularity bias. In this work, we argue that popularity bias often leads to other biases that are not obvious when additional user or item information is not provided to the researcher. We examine our hypothesis in the book recommendation case on a commonly used dataset with book ratings. We enrich it with author information using publicly available external sources. We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles. We conclude that the societal implications of popularity bias should be further examined by the scholar community.
