Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimize for personalized user experience, they can overlook impacts on cultural experience in the aggregate. After demonstrating that existing metrics do not center culture, we introduce a new metric, commonality, that measures the degree to which recommendations familiarize a given user population with specified categories of cultural content. We developed commonality through an interdisciplinary dialogue between researchers in computer science and the social sciences and humanities. With reference to principles underpinning public service media systems in democratic societies, we identify universality of address and content diversity in the service of strengthening cultural citizenship as particularly relevant goals for recommender systems delivering cultural content. We develop commonality as a measure of recommender system alignment with the promotion of content toward a shared cultural experience across a population of users. We empirically compare the performance of recommendation algorithms using commonality with existing metrics, demonstrating that commonality captures a novel property of system behavior complementary to existing metrics. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing `public good' rationales for machine learning systems.
翻译:建议系统已成为整理文化内容的主要手段,极大地影响个人文化经验。由于建议系统倾向于优化个人化用户经验,因此可以忽略对总体文化经验的影响。在证明现有衡量标准不以文化为中心之后,我们引入了新的衡量标准、共性,衡量建议使特定用户群体熟悉特定文化内容类别的程度。我们通过计算机科学和社会科学及人文学的研究者之间的跨学科对话发展了共同性。关于民主社会公共服务媒体系统的基本原则,我们确定,在加强文化公民意识服务方面,地址和内容多样性的普遍性是提供文化内容的建议系统特别相关的目标。我们发展共同性,作为建议系统与促进内容在用户群体中共享文化经验的衡量标准。我们用与现有衡量标准的共性对建议算法的绩效进行实证比较,表明共性反映了与现有衡量标准相辅相成的系统行为的新特性。除了现有的公平和多样性衡量标准外,共同性有助于不断增长的奖学金体系发展`公共良好'理由。