用排序对齐提升个性化题目推荐效率
Personalized Education with Ranking Alignment Recommendation
- 将协同思想融入探索机制，优化强化学习中的题目选择
- 在有限训练轮次内显著提升推荐准确率
- 适用于各类基于强化学习的题目推荐系统
个性化题目推荐旨在引导学生通过合适题目提升学习目标掌握度。以往方法多将此任务建模为马尔可夫决策过程并使用强化学习求解，但存在探索效率低的问题，难以在训练过程中找到最适合每位学生的最优题目。为此，我们提出排序对齐推荐（Ranking Alignment Recommendation, RAR），将协同思想引入探索机制，在有限训练轮次内实现更高效的探索。实验表明，RAR能有效提升推荐性能，且该框架可适配任意基于强化学习的题目推荐器。代码已开源：https://github.com/wuming29/RAR.git。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。