  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.
