个性化推荐中基于个体公平的学习机会均等
Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations
摘要：
在线教育平台在促进个人职业成功方面发挥着主要作用。因此，在构建覆盖式内容推荐服务的同时，必须根据平台价值观、背景和教学方法，确保为学习者提供平等的推荐学习机会。虽然确保学习机会平等的重要性在传统机构中得到了很好的研究，但如何通过推荐系统在在线学习生态系统中实现这种平等仍然没有得到充分的探讨。在本文中，我们形式化的教育原则，模型建议的学习属性，和一个新的公平性度量，将它们结合起来，以监测平等的学习者之间的推荐学习机会。然后，我们设想一个场景，其中教育平台应该以这样一种方式进行安排，即生成的建议在一定程度上满足所有学习者的每个原则，并限制到他们的个人偏好。在这种观点下，我们探讨了在一个大规模的课程平台推荐系统提供的学习机会，发现系统的不平等。为了减少这种影响，我们提出了一种新型的后处理方法，可以平衡个性化和推荐机会的平等性。实验表明，我们的方法导致更高的平等，在个性化的损失可以忽略不计。我们的研究在将人类学习的道德规范应用于建议方面向前迈进了一步，建议是智能教育系统的核心单元。
英文摘要：
Online educational platforms are playing a primary role in mediating the success of individuals' careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform values, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize educational principles that model recommendations' learning properties, and a novel fairness metric that combines them in order to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a large-scale course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. Our study moves a step forward in operationalizing the ethics of human learning in recommendations, a core unit of intelligent educational systems.
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。
