论文标题
推荐系统和算法仇恨
Recommender Systems and Algorithmic Hate
论文作者
论文摘要
尽管越来越依赖数字平台的个性化，但许多策划用户信息或信息的算法都具有阻力。当用户感到不满意或受到建议的伤害时，这可能会导致用户讨厌或对这些个性化系统感到负面。算法的仇恨对用户和系统都有不利影响，并可能导致各种形式的算法伤害，或者在极端情况下，可能导致公众抗议“算法”。在这项工作中，我们通过对个性化推荐系统的各种案例研究来总结算法仇恨及其负面后果的一些最常见原因。我们探索了Recsys研究社区的有希望的未来方向，这些方向可以帮助减轻算法仇恨并改善推荐系统与其用户之间的关系。
Despite increasing reliance on personalization in digital platforms, many algorithms that curate content or information for users have been met with resistance. When users feel dissatisfied or harmed by recommendations, this can lead users to hate, or feel negatively towards these personalized systems. Algorithmic hate detrimentally impacts both users and the system, and can result in various forms of algorithmic harm, or in extreme cases can lead to public protests against ''the algorithm'' in question. In this work, we summarize some of the most common causes of algorithmic hate and their negative consequences through various case studies of personalized recommender systems. We explore promising future directions for the RecSys research community that could help alleviate algorithmic hate and improve the relationship between recommender systems and their users.
