基于排序对齐推荐的个性化教育
Personalized Education with Ranking Alignment Recommendation
AI总结：
针对现有基于强化学习的个性化习题推荐探索效率低、难筛选最优习题的问题，提出融入协同思想的排序对齐推荐方法，有效提升推荐性能且可适配各类RL习题推荐框架。
摘要：
个性化习题推荐旨在引导学生通过习题练习强化对学习目标的掌握。现有多数方法将该任务建模为马尔可夫决策过程并采用强化学习求解，但存在探索效率低的问题，训练过程中无法为每位学生筛选出最优习题。为此，我们提出排序对齐推荐（Ranking Alignment Recommendation，RAR），将协同思想融入探索机制，在有限训练轮次内实现更高效的探索。实验表明，RAR有效提升了推荐性能，且该框架可适配任意基于强化学习的习题推荐器。代码已开源至https://github.com/wuming29/RAR.git。
英文摘要：
Personalized question recommendation aims to guide individual students through questions to enhance their mastery of learning targets. Most previous methods model this task as a Markov Decision Process and use reinforcement learning to solve, but they struggle with efficient exploration, failing to identify the best questions for each student during training. To address this, we propose Ranking Alignment Recommendation (RAR), which incorporates collaborative ideas into the exploration mechanism, enabling more efficient exploration within limited training episodes. Experiments show that RAR effectively improves recommendation performance, and our framework can be applied to any RL-based question recommender. Our code is available in https://github.com/wuming29/RAR.git.
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。
