学习信任：理解教育推荐系统中的编辑权威和信任
Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education
摘要：
推荐系统（RS）中的信任通常通过使用用户感知的可信社交邻居的隐式或显式反馈在算法上合并，并使用用户报告的推荐项目的可信度进行评估。然而，现实生活中的推荐设置可以在信任，权力和特权方面表现出群体差异。我们的研究探讨了一个互补的信任，这依赖于编辑权力关系和RS应用领域的所有利益相关者的态度。我们设计了一个简单的，第一原则的编辑权威度量，即，用户对推荐源的偏好、否决权和合并用户反馈，使得一个RS用户组通过放弃或分配编辑权限而将信任授予另一个RS。在弗吉尼亚理工大学的一项混合方法研究中，我们调查了教师、助教和学生对编辑权威的偏好，并假设测试了其与假设的"建议阅读" RS算法的信任关系。我们发现，更高的RS编辑权限分配给学生的相对信任的课程人员分配给RS算法和学生。我们还观察到，当然工作人员赞成更高的控制RS算法在采购和更新的建议长期。使用内容分析，我们讨论了频繁的工作人员推荐的学生编辑角色，并强调他们的常见理由，如感知的专业知识，缩放学习环境，专业课程的需求，和学习者脱离。我们认为，我们的分析突出了关键的用户偏好，以帮助检测编辑权力不对称，并确定RS用例，以支持教学和研究
英文摘要：
Trust in a recommendation system (RS) is often algorithmically incorporated using implicit or explicit feedback of user-perceived trustworthy social neighbors, and evaluated using user-reported trustworthiness of recommended items. However, real-life recommendation settings can feature group disparities in trust, power, and prerogatives. Our study examines a complementary view of trust which relies on the editorial power relationships and attitudes of all stakeholders in the RS application domain. We devise a simple, first-principles metric of editorial authority, i.e., user preferences for recommendation sourcing, veto power, and incorporating user feedback, such that one RS user group confers trust upon another by ceding or assigning editorial authority. In a mixed-methods study at Virginia Tech, we surveyed faculty, teaching assistants, and students about their preferences of editorial authority, and hypothesis-tested its relationship with trust in algorithms for a hypothetical `Suggested Readings' RS. We discover that higher RS editorial authority assigned to students is linked to the relative trust the course staff allocates to RS algorithm and students. We also observe that course staff favors higher control for the RS algorithm in sourcing and updating the recommendations long-term. Using content analysis, we discuss frequent staff-recommended student editorial roles and highlight their frequent rationales, such as perceived expertise, scaling the learning environment, professional curriculum needs, and learner disengagement. We argue that our analyses highlight critical user preferences to help detect editorial power asymmetry and identify RS use-cases for supporting teaching and research
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
