Personalized Education with Ranking Alignment Recommendation

• URL: http://arxiv.org/abs/2507.23664v1
• Date: Thu, 31 Jul 2025 15:43:51 GMT
• Title: Personalized Education with Ranking Alignment Recommendation
• Authors: Haipeng Liu, Yuxuan Liu, Ting Long,
• Abstract summary: We propose Ranking Alignment Recommendation (RAR), which incorporates collaborative ideas into the exploration mechanism.<n>RAR effectively improves recommendation performance, and our framework can be applied to any RL-based question recommender.
• Score: 6.5786507866287325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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