Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation

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