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.

翻译：随着在线教育系统的发展，个性化教育推荐已发挥重要作用。本文聚焦于路径推荐系统的开发，旨在为每个会话中的给定用户生成并推荐完整的学习路径。针对现有方法未能考虑路径中概念相关性的问题，我们提出了一种名为"基于集合到序列排序的认知感知学习路径推荐"（SRC）的新型框架，该框架将推荐任务形式化为集合到序列范式。具体而言，我们首先设计了一个认知感知编码器模块，该模块能够捕获输入学习概念之间的相关性。随后，输出被馈送到解码器模块，该模块通过注意力机制顺序生成路径，处理学习概念与目标概念之间的相关性。我们的推荐策略通过策略梯度进行优化。此外，我们还引入了一个基于知识追踪的辅助模块，通过评估学生对学习概念的学习效果来增强模型的稳定性。我们在两个真实世界公共数据集和一个工业数据集上进行了大量实验，实验结果表明了SRC的优越性和有效性。代码将发布于 https://gitee.com/mindspore/models/tree/master/research/recommend/SRC。