# Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

arXiv: https://arxiv.org/abs/2306.04234 · HTML: https://mlsanity.com/paper/2306.04234/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2023.

## Built on by (most cited first, 9 of 9)

- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)
- [Towards Goal-oriented Intelligent Tutoring Systems in Online Education](https://mlsanity.com/paper/2312.10053/) (2023)
- [Versatile Ordering Network: An Attention-based Neural Network for Ordering Across Scales and Quality Metrics](https://mlsanity.com/paper/2412.12759/) (2024)
- [Enhancing Learning Path Recommendation via Multi-task Learning](https://mlsanity.com/paper/2507.05295/) (2025)
- [Personalized Education with Ranking Alignment Recommendation](https://mlsanity.com/paper/2507.23664/) (2025)
- [Constructing a Question-Answering Simulator through the Distillation of LLMs](https://mlsanity.com/paper/2509.09226/) (2025)
- [CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillation](https://mlsanity.com/paper/2511.17041/) (2025)
- [Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion](https://mlsanity.com/paper/2604.14613/) (2026)
- [UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation](https://mlsanity.com/paper/2605.16750/) (2026)

## Abstract (arXiv)

With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm. Specifically, we first design a concept-aware encoder module which can capture the correlations among the input learning concepts. The outputs are then fed into a decoder module that sequentially generates a path through an attention mechanism that handles correlations between the learning and target concepts. Our recommendation policy is optimized by policy gradient. In addition, we also introduce an auxiliary module based on knowledge tracing to enhance the model's stability by evaluating students' learning effects on learning concepts. We conduct extensive experiments on two real-world public datasets and one industrial dataset, and the experimental results demonstrate the superiority and effectiveness of SRC. Code will be available at https://gitee.com/mindspore/models/tree/master/research/recommend/SRC.