Personalized Education with Ranking Alignment Recommendation
This work addresses a specific bottleneck in personalized education systems for students, representing an incremental improvement over existing methods.
The paper tackles the problem of inefficient exploration in reinforcement learning-based personalized question recommendation by proposing Ranking Alignment Recommendation (RAR), which incorporates collaborative ideas to improve exploration, resulting in enhanced recommendation performance as demonstrated in experiments.
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.