# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
###### Abstract
In scenarios with limited computational resources, more important information can be processed with finite computational resources. For online course recommendations, although a significant amount of research has been conducted, existing studies still suffer from several issues. Current research primarily employs machine learning and deep learning methods to model online courses while neglecting sequential relationships between courses. Few studies utilize multi-modal heterogeneous data to deeply investigate personalized course recommendations through hypergraphs and attention mechanisms. There is limited exploration of common learning behaviors and similar learning interactions among different learners, with insufficient research on personalized recommendations from the perspective of global relational graphs. Traditional course recommendation models assign equal weights to each learner and recommended item, and their node embedding methods overlook the varying importance of nodes by connecting and projecting all nodes into the same space. In reality, different nodes may have distinct significance, for instance, beginners and advanced learners likely prioritize different course content (e.g., programming novices versus algorithm engineers may exhibit significant differences in their demand weights for Python courses). To address these challenges, this study makes the following contributions:  
1. We propose a heterogeneous hypergraph and attention mechanism-based online course recommendation algorithm (HHAOCR). By constructing a heterogeneous hypergraph with entities including students, courses, and instructors, our model globally explores sequential relationships, co-learning relationships, and similarity relationships among courses. Compared to traditional recommendation models, this framework more comprehensively captures inter-entity connections, thereby enhancing recommendation accuracy. 2. To tackle the difficulty in discovering high-order connectivity and nonlinear relationships within multi-modal heterogeneous data in online learning scenarios, we designed five heterogeneous hypergraphs using hypergraph convolution operators. Each vertex aggregates information through its associated hyperedges, enabling the model to more accurately capture high-order relationships among students, instructors, and courses. 3. We propose a fusion method integrating heterogeneous hypergraphs with attention mechanisms. By combining attention mechanisms with hypergraph convolution operators, we develop a dynamically adaptive attention mechanism. This includes a coefficient matrix calculation method between hyperedges and vertices, as well as an attention score computation method between hyperedges, to identify the most relevant nodes or edges in the graph. This mechanism allows dynamic weight adjustment during feature extraction, enabling the model to focus precisely on critical information and relationships, thereby significantly improving the personalization and precision of recommendations. The rest of this paper is organized as follows: Section 2 presents the fundamental preliminaries and preparatory work. Section 3 provides the formal definition and comprehensive description of our proposed algorithm. Section 4 details the experimental datasets and methodological procedures; then, analyzes and discusses the experimental results. Section 5 concludes the study with key findings. Finally, Section 6 critically examines the limitations of this work and outlines potential directions for future research.