用生存分析预测学习者退课时间，提升课程推荐精准度
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
- 将生存分析引入协同过滤，建模学习者退课/完成时间
- 在三个公开数据集上，推荐效果优于传统协同过滤方法
- 适合做MOOC个性化推荐系统的研究者与教育平台开发者
大规模在线开放课程（MOOCs）虽提供灵活学习机会，但大量注册用户最终退课。为提升学习参与度，需推荐符合学习者偏好与需求的课程。课程推荐系统可通过建模学习者历史交互行为来实现个性化推荐。本研究提出一种新方法，利用生存分析（SA）建模MOOC中的时间至退课和时间至完成等事件，以增强协同过滤推荐性能。实验基于三个公开数据集，在两项评估指标上验证了该方法显著优于仅依赖学习者交互行为的传统协同过滤模型。结果表明，将生存分析与推荐系统结合，能有效提升MOOC场景下的个性化推荐能力。
原文摘要 · Abstract (English)
Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various disciplines, anytime and anywhere. Despite this accessibility, a significant number of enrollments in MOOCs result in dropouts. To enhance learner engagement, it is crucial to recommend courses that align with their preferences and needs. Course Recommender Systems (RSs) can play an important role in this by modeling learners' preferences based on their previous interactions within the MOOC platform. Time-to-dropout and time-to-completion in MOOCs, like other time-to-event prediction tasks, can be effectively modeled using survival analysis (SA) methods. In this study, we apply SA methods to improve collaborative filtering recommendation performance by considering time-to-event in the context of MOOCs. Our proposed approach demonstrates superior performance compared to collaborative filtering methods trained based on learners' interactions with MOOCs, as evidenced by two performance measures on three publicly available datasets. The findings underscore the potential of integrating SA methods with RSs to enhance personalization in MOOCs.
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