标题： 基于排名对齐的个性化教育推荐 显示英文标题

标题： Personalized Education with Ranking Alignment Recommendation

摘要： 个性化问题推荐旨在通过问题引导个别学生，以增强他们对学习目标的掌握。 大多数以前的方法将此任务建模为马尔可夫决策过程，并使用强化学习来解决，但它们在高效探索方面存在困难，在训练期间无法识别每个学生的最佳问题。 为了解决这个问题，我们提出了排序对齐推荐（RAR），它将协作思想引入探索机制，使得在有限的训练回合内能够更高效地探索。 实验表明，RAR有效提高了推荐性能，我们的框架可以应用于任何基于强化学习的问题推荐器。 我们的代码可在 https://github.com/wuming29/RAR.git 获取。 显示英文摘要

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.