# NR4DER: Neural Re-ranking for Diversified Exercise Recommendation

arXiv: https://arxiv.org/abs/2506.06341 · HTML: https://mlsanity.com/paper/2506.06341/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval](https://mlsanity.com/paper/1912.05891/) (2019)
- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.14170/) (2024)
- [Long-tail Session-based Recommendation](https://mlsanity.com/paper/2007.12329/) (2020)
- [LTE4G: Long-Tail Experts for Graph Neural Networks](https://mlsanity.com/paper/2208.10205/) (2022)
- [Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation](https://mlsanity.com/paper/2106.06165/) (2021)
- [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](https://mlsanity.com/paper/1805.08524/) (2018)
- [MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation](https://mlsanity.com/paper/2304.08382/) (2023)
- [Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2010.11401/) (2020)
- [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://mlsanity.com/paper/1902.00245/) (2019)
- [IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2403.19881/) (2024)
- [CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation](https://mlsanity.com/paper/2105.10868/) (2021)
- [Next-item Recommendations in Short Sessions](https://mlsanity.com/paper/2107.07453/) (2021)

## Built on by (most cited first, 5 of 5)

- [LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation](https://mlsanity.com/paper/2602.17036/) (2026)
- [U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation](https://mlsanity.com/paper/2602.23400/) (2026)
- [BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation](https://mlsanity.com/paper/2602.15879/) (2026)
- [UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation](https://mlsanity.com/paper/2605.16750/) (2026)
- [ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs](https://mlsanity.com/paper/2608.03006/) (2026)

## Abstract (arXiv)

With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). NR4DER first leverages the mLSTM model to improve the effectiveness of the exercise filter module. It then employs a sequence enhancement method to enhance the representation of inactive students, accurately matches students with exercises of appropriate difficulty. Finally, it utilizes neural re-ranking to generate diverse recommendation lists based on individual students' learning histories. Extensive experimental results indicate that NR4DER significantly outperforms existing methods across multiple real-world datasets and effectively caters to the diverse learning pace of students.