# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 2 Preliminaries
### Hypergraphs  
In traditional graph neural network structures, data connections are pairwise. However, real-world data structures often extend beyond pairwise connections and can be much more complex, especially in online learning scenarios involving multimodal data. To fully explore and capture the associations between multimodal data structures, more complex data structures are needed for modeling. Although methods based on graph neural networks for course recommendation have achieved certain results, these GNNs-based models are unable to capture complex high-order user relationship patterns beyond pairwise connections, and they still face limitations when processing high-order information.  
Using hypergraphs [25; 26] for modeling can effectively address the above issues. A hypergraph is a generalized graph structure that extends the concept of edges to connect more than two nodes. A hypergraph [27] is a non-pairwise data structure that can represent high-order complex relationships. Compared to traditional graph structures, hypergraph structures have greater advantages in modeling data relationships. As a result, recommendation systems based on hypergraphs have attracted the attention of scholars and have been applied to various recommendation scenarios, such as social recommendation and group recommendation [28; 29]. Although hypergraph learning has significant advantages in modeling complex high-order data relationships, few studies have applied hypergraphs to online course recommendations, fully exploring high-order nonlinear relationships among students, courses, and teachers for course recommendations.  
In traditional online course recommendations, most studies overlook the order of course recommendations and students' preferences, such as their preferences for specific instructors. In fact, course sequences often include the learning paths and patterns that must be followed to master a subject. For example, a computer science learner who follows this course sequence: Computer Programming Languages and Hardware -> Data Structures and Algorithms -> Operating Systems -> Software Engineering -> Compilers -> Big Data and Artificial Intelligence, is more likely to complete all the courses in the computer science major than another learner who starts with compilers or big data and artificial intelligence. This can reduce the likelihood of learners feeling overwhelmed due to inappropriate recommendations, which may lead them to abandon their entire learning plan and increase dropout rates. This situation is particularly prominent in the study of STEM (Science, Technology, Engineering, and Mathematics) [29; 30; 31] courses. Additionally, naturally, the content and teaching style of online courses offered by instructors may be preferred by different students. Therefore, different students may choose completely different instructors for the same course. Consequently, if two users choose similar instructor courses during the early stages of their learning process, it is reasonable to assume that they are more likely to continue choosing other courses from the same instructors in the future. Based on these issues, this chapter proposes an online course recommendation method based on hypergraphs and attention mechanisms to improve the rationality of course recommendations.